IROS 2011 Awards - Lirmm

2011 IEEE/RSJ International Conference 

on Intelligent Robots and Systems 

September 25-30, 2011 

San Francisco, USA 

Celebrating 50 Years of Robotics 

Co-Sponsoring Societies 

Corporate Sponsors

Foreword 

The spring of 1961 marks an important milestone in the history of robotics. The world first 

working robot, the Unimate, developed by Devol and Engelberger, was deployed in the 

assembly line at General Motors for die casting handling. The centuries-old robot concept 

finally came to fruition in the form of a working machine in production. Fifty years later 

today, robotics is expanding beyond its success in manufacturing. Our field is entering a 

new era of human-centered developments attracting collaborations and interactions 

between diverse research communities from different disciplines. 

IROS 2011 is both a celebration and a renewal. Celebrating the achievements of the field, 

the 2011 edition focuses on human-centered robotics bringing a wide coverage of its subfields 

and diverse communities. IROS 2011 was designed to achieve the highest quality in 

technical content and to promote discussion and social interactions. A major feature of this 

conference is the interactive presentation model. Authors of selected contributions are 

given the opportunity to first outline their work during regular sessions and then to fully 

engage small groups of conference attendees, presenting and discussing their work using 

videos, slides, and simulators. With the interactive presentations, the number of parallel 

tracks is significantly reduced, thus promoting better interaction among the participants. 

The conference also features a significant number of special-topic symposia; each 

associated with an active research area of robotics introduced by an invited expert. 

In addition, the program includes robot demonstrations, thematic plenaries on design, biorobotics, 

and intelligent transportation, and four forums: (i) Robots, the Next Generation; (ii) 

Medical Robotics; (iii) Robots, the New Platforms; and (iv) Robotics: Beyond the Horizon. 

The program also includes a Town Hall Meeting on Robotics Conferences, 27 workshops 

and tutorials, technical tours, and a large exhibition showing some of the latest 

developments in the field. 

IROS 2011 received 2459 paper submissions. The reviews were carried out by the 

Conference Editorial Board (CEB), which involved 9 editors, 258 associate editors, and 

over 3500 reviewers coordinated by the Editor-in-Chief. At its meeting, the Senior Program 

Committee discussed the submissions and their corresponding reviews and CEB 

recommendations, and finally selected 790 papers for presentation at the conference, 

organized in 130 sessions in ten parallel tracks. 

A vibrant social program including four events: a Cruise on the San Francisco Bay, an 

Evening at the de Young Museum, a Welcome, and a Farewell Reception. This program is 

designed to provide an atmosphere for fruitful discussions and meaningful interactions 

among the participants. 

We would like to take this opportunity to express our thanks and appreciations to all the 

members of the Organizing Committee and the Conference Editorial Board for the effort 

they devoted to the program and organization of this conference. Our special thanks go to 

the volunteers and staffs for the long hours and hard work they have generously given to 

the conference. Above all, IROS 2011 is the result of its technical contributions, and we 

would like to thank all the authors, speakers, and participants for taking part in and 

contributing to this conference. We warmly welcome you to San Francisco! 

Oussama Khatib Gaurav Sukhatme 

General Chair, IROS 2011 Program Chair, IROS 2011

Foreword 

Table of Contents 

IROS Organization .…...………………………………………………………………..… ii 

IROS 2011 Organizing Committee .…………………………………………………….. iii 

IROS 2011 Conference Editorial Board .……………………………………………….. v 

IROS 2011 Reviewers .…………………………………………………………………… viii 

Plenary Sessions .………………………………………………………………………… xxii 

Workshops and Tutorials .………………………………………………………..……… xxv 

Demonstration Sessions .………………………………………………………………... xxviii 

Exhibition .………………………………………………………………………………..... xxxiv 

IROS 2011 Corporate Sponsors ..………………………………………………………. xxxvi 

IROS 2011 Awards ……………....………………………………………………………. xxxvii 

Social Events .…………………………………………………………………………….. xxxviii 

Map of Conference Venue .……………………………………………………………… xl 

Conference Overview .…………………………………………………………………… xli 

Technical Program at a Glance 

Monday, September 26 ..…………………………………………………………... xliii 

Tuesday, September 27 ..………………………………………………...………... xliv 

Wednesday, September 28 .……………………………………………………….. xlv 

Thurdsay, September 29 .....…………………………………………………..….... xlvi 

Technical Program Digest 

Monday, September 26 ..……………………………………...…………………… 1 

Tuesday, September 27.…..………………………………………………...……… 39 

Wednesday, September 28 ..….….………………………………………………... 93 

Thursday, September 29 …..………………………………………………..……… 167 

Author Index ..……………………………………………………………………………... 231 

Keyword Index ..…………………………………………………………………………… 261 

Corporate Sponsors Ads 

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems 

–i–

IROS Advisory/Steering Committee 

IROS Organization 

Advisory Council Founding Honorary Chair 

Fumio Harashima, Tokyo Metropolitan University, Japan 

Past Advisory Council Honorary Chair 

Tzyh-Jong Tarn, Washington University, USA 

Advisory Council Honorary Chair 

C.S. George Lee, Purdue University, USA 

Advisory Council Chair 

Toshio Fukuda, Nagoya University, Japan 

Advisory Council Vice Chair 

Shin'ichi Yuta, University of Tsukuba, Japan 


–ii– 

Co-sponsoring Societies 

IEEE Robotics and Automation Society (RAS) 

www.ieee-ras.org 

IEEE Industrial Electronics Society (IES) 

www.ieee-ies.org 

Robotics Society of Japan (RSJ) 

www.rsj.or.jp 

Society of Instrument and Control Engineers (SICE) 

www.sice.or.jp 

New Technology Foundation (NTF) 

www.ntf.or.jp 

Technical Co-sponsoring Society 

Institute of Control, Robotics and Systems (ICROS) 

www.icros.org

General Chair 

IROS 2011 Organizing Committee 

Oussama Khatib, Stanford University, USA 

Program Chair 

Gaurav Sukhatme, University of Southern California, USA 

Program Co-Chair 

Roland Siegwart, Swiss Federal Institute of Technology Zurich, Switzerland 

CEB Chair 

Nancy M. Amato, Texas A&M University, USA 

50 Year of Robotics Symposia 

Vijay Kumar (Chair), University of Pennsylvania, USA 

Henrik Christensen, Georgia Institute of Technology, USA 

Oliver Brock, Technische Universität Berlin, Germany 

Interactive Presentations 

Daniela Rus (Chair), Massachusetts Institute of Technology, USA 

Peter Corke, Queensland University of Technology, Australia 

Awards 

Raja Chatila (Chair), Centre National de la Recherche Scientifique, France 

Shigeki Sugano, Waseda University, Japan 

Allison Okamura, The Johns Hopkins University, USA 

Workshops & Tutorials 

C.S. George Lee (Chair), Purdue University, USA 

Christian Laugier, INRIA Rhône-Alpes, France 

Shin’ichi Yuta, University of Tsukuba, Japan 

Local Arrangements 

Torsten Kroeger (Chair), Stanford University, USA 

Francois Conti, Stanford University, USA 


–iii–

Finance Chair 

Xiaoping Yun, Naval Postgraduate School, USA 

Publications Chair 

Alessandro De Luca, Università di Roma "La Sapienza", Italy 

Exhibits 

Kurt Konolige (Chair), Willow Garage, USA 

Luis Sentis, University of Texas at Austin, USA 

Kai Oliver Arras, Albert-Ludwigs-Universität Freiburg, Germany 

Publicity 

Aude Billard (Chair), Swiss Federal Institute of Technology Lausanne, Switzerland 

Ken Goldberg, University of California Berkeley, USA 

Yoshi Nakamura, University of Tokyo, Japan 

Student Travel Awards 

Dezhen Song (Chair), Texas A&M University, USA 

M. Ani Hsieh, Drexel University, USA 

Dylan Shell, Texas A&M University, USA 

Technical Tours 

Erin Rapacki, Adept Technology, Inc., USA 

Senior Program Committee 

Marcelo Ang, National University of Singapore, Singapore 

Wolfram Burgard, Albert-Ludwigs-Universität Freiburg, Gemrany 

Mark Cutkosky, Stanford University, USA 

Rüdiger Dillmann, Karlsruhe Institute of Technology, Germany 

Ken Goldberg, University of California Berkeley, USA 

Makoto Kaneko, Osaka University, Japan 

Lydia Kavraki, Rice University, USA 

Dong-soo Kwon, Korea Advanced Institute of Science and Technology, Korea 

Kewin Lynch, Northwestern University, USA 

Eduardo nebot, University of Sydney, Australia 

Brad Nelson, Swiss Federal Institute of Technology Zurich, Switzerland 

Bruno Siciliano, Università degli Studi di Napoli Federico II, Italy 

Satoshi Tadokoro, Tohoku University, Japan 

Seth Hutchinson, University of Illinois at Urbana-Champaign, USA 

Stefan Schaal, University of Southern California, USA 


–iv–

IROS 2011 Conference Editorial Board 

Editor-in-Chief 

Nancy M. Amato, Texas A&M University, USA 

Editors 

I-Ming Chen, Nanyang Technological University, Singapore 

Alessandro De Luca, Università di Roma "La Sapienza", Italy 

Chad Jenkins, Brown University, USA 

Danica Kragic, Royal Institute of Technology, Sweden 

Nikos Papanikolopoulos, University of Minnesota, USA 

Frank Park, Seoul National University, Korea 

Lynne Parker, University of Tennessee, USA 

Shigeki Sugano, Waseda University, Japan 

Frank van der Stappen, Utrecht University, Netherlands 

Associate Editors 

Pieter Abbeel, UC Berkeley, USA 

Farhad Aghili, Canadian Space Agency, Canada 

Aaron Ames, Texas A&M Univ., USA 

Yacine Amirat, Univ. Paris Est Créteil, France 

Monica Anderson, Univ. of Alabama, USA 

Juan Andrade-Cetto, CSIC-UPC, Spain 

Wei Tech Ang, Nanyang Tech. Univ., Singapore 

Adnan Ansar, JPL, USA 

Gianluca Antonelli, Univ. Cassino, Italy 

Hirohiko Arai, AIST, Japan 

Helder Araujo, Univ. Coimbra, Portugal 

Kai Oliver Arras, Univ. Freiburg, Germany 

Panagiotis Artemiadis, MIT, USA 

Tamim Asfour, KIT, Germany 

Olivier Aycard, Joseph Fourier Univ., France 

Jan Babic, Inst. Jozef Stefan, Slovenia 

Shaoping Bai, Aalborg Univ., Denmark 

Devin Balkcom, Dartmouth College, USA 

Patrick Beeson, TracLabs Inc, USA 

Michael Beetz, TU Munich, Germany 

Sven Behnke, Univ. Bonn, Germany 

Kostas Bekris, Univ. of Nevada-Reno, USA 

Maren Bennewitz, Univ. Freiburg, Germany 

Ryad Benosman, UPMC/ISIR, France 

Dmitry Berenson, CMU, USA 

Sarah Bergbreiter, Univ. of Maryland, USA 

Philippe Bidaud, UPMC, Paris 6, France 

Nathaniel Bird, Ohio Northern University, USA 

Christoph Borst, DLR, Germany 

Alan Bowling, Univ. of Texas Arlington, USA 

Tim Bretl, Univ. of Illinois Urbana, USA 

Michael Bruenig, CSIRO, Australia 

Etienne Burdet, Imperial College London, UK 


–v– 

Wolfram Burgard, Univ. Freiburg, Germany 

Darius Burschka, TU Munich, Germany 

David Cappelleri, Stevens Inst. of Tech., USA 

Giuseppe Carbone, Univ. Cassino, Italy 

Stefano Carpin, Univ. of California Merced, USA 

Alicia Casals, UPC, Spain 

Jose Castellanos, Univ. Zaragoza, Spain 

Francois Chaumette, RIA Rennes, France 

XiaoQi Chen, Univ. Canterbury, New Zealand 

Sonia Chernova, Worcester Polytech. Inst., USA 

Chee Meng Chew, National Univ., Singapore 

Kiyo Chinzei, AIST, Japan 

Sachin Chitta, Willow Garage Inc., USA 

Kyu-Jin Cho, Seoul National Univ., Korea 

Youngjin Choi, Hanyang Univ., Korea 

Howie Choset, CMU, USA 

Changmook Chun, KIST, Korea 

Timothy Chung, Naval Postgraduate School, USA 

Woojin Chung, Korea Univ., Korea 

Chris Clark, California Polytech. State Univ., USA 

Nikolaus Correll, Univ. of Colorado Boulder, USA 

Juan Cortes, LAAS, France 

Jacob Crandall, MIT/Masdar, USA 

Elizabeth Croft, Univ. of British Columbia, Canada 

Jian Dai, King's College London, UK 

Aveek Das, Sarnoff Corporation, USA 

Angel del Pobil, Univ. Jaume I, Spain 

Jorge Dias, Univ. Coimbra, Portugal 

Ryan Eustice, Univ. of Michigan, USA 

Manuel Ferre, UP Madrid, Spain 

Antoine Ferreira, Univ. Orleans, France 

Nicola Ferrier, Univ. of Wisconsin Madison, USA 

Paolo Fiorini, Univ. Verona, Italy

Robert Fitch, Univ. Sydney, Australia 

Thierry Fraichard, INRIA Rhone-Alpes, France 

Emilio Frazzoli, MIT, USA 

Eric Frew, Univ. of Colorado, USA 

Antonio Frisoli, Scuola Superiore Sant'Anna, Italy 

Nicholas Gans, Univ. of Texas Dallas, USA 

Roland Geraerts, Utrecht Univ., Netherlands 

Christopher Geyer, iRobot Corp., USA 

Bill Goodwine, Notre Dame Univ., USA 

Ambarish Goswami, Honda Research Inst., USA 

Giorgio Grisetti, Univ. Freiburg, Germany 

Josechu Guerrero, Univ. Zaragoza, Spain 

Eugenio Gugliemelli, Campus Bio-Medico, Italy 

Yi Guo, Stevens Inst. of Technology, USA 

Kamal Gupta, Simon Fraser Univ., Canada 

Li Han, Clark Univ., USA 

Kensuke Harada, AIST, Japan 

Kris Hauser, Indiana Univ., USA 

Shinichi Hirai, Ritsumeikan Univ., Japan 

Yasuhisa Hirata, Tohuku Univ., Japan 

Koh Hosoda, Osaka Univ., Japan 

Franz Hover, MIT, USA 

Stefan Hrabar, CSIRO, Australia 

M. Ani Hsieh, Drexel Univ., USA 

Wes Huang, iRobot Corp., USA 

Javier Ibanez-Guzman, Renault, France 

Tetsunari Inamura, Nat’l Inst. of Informatics, Japan 

Kazuo Ishii, Kyushu Inst. of Technology, Japan 

Volkan Isler, Univ. of Minnesota, USA 

Hiroyasu Iwata, Waseda Univ., Japan 

Michael Jakuba, Univ. Sydney, Australia 

Patric Jensfelt, Royal Inst. of Technology, Sweden 

Rolf Johansson, Lund Univ., Sweden 

Chris Jones, iRobot Corp., USA 

Ming-Shaun Ju, Nat’l Cheng Kung Univ., Taiwan 

Simon Julier, University College London, UK 

Takayuki Kanda, ATR, Japan 

Balajee Kannan, Carnegie Mellon Univ., USA 

Peter Kazanzides, Johns Hopkins Univ., USA 

Abderrahmane Kheddar, CNRS, France/Japan 

Ryo Kikuuwe, Kyushu Univ., Japan 

Xianwen Kong, Heriot-Watt Univ., UK 

Jana Kosecka, George Mason Univ., USA 

Hadas Kress-Gazit, Northwestern Univ., USA 

Venkat Krovi, Univ. Buffalo (SUNY), USA 

Naoyuki Kubota, Tokyo Metropolitan Univ., Japan 

Takashi Kubota, JAXA, Japan 

James Kuffner, CMU, USA 

Dana Kulic, Univ. Waterloo, Canada 

Daisuke Kurabayashi, Tokyo Inst. of Tech., Japan 

Hanna Kurniawati, SMART, Singapore 

Kostas Kyriakopoulos, NTU Athens, Greece 

Ville Kyrki, Lappeenranta Univ. of Tech., Finland 

Dongjun Lee, Univ. of Tennessee, USA 

Ji Yeong Lee, Hanyang Univ., Korea 

Weihua Li, Univ. Wolloongong, Australia 

Xingyan Li, Univ. Auckland, New Zealand 


–vi– 

Yangmin Li, Univ. of Macau, Macau 

Jyh-Ming Lien, George Mason Univ., USA 

Maxim Likhachev, CMU, USA 

Achim Lilienthal, Orebro Univ., Sweden 

Pedro Lima, Lisbon Technical Univ., Portugal 

Dikai Liu, Univ. of Technology Sydney, Australia 

Honghai Liu, Univ. Portsmouth, UK 

Peter X. Liu, Carleton Univ., Canada 

Xin-Jun Liu, Tsinghua Univ., China 

Savvas Loizou, Cyprus Univ. of Tech., Cyprus 

Meghann Lomas, Lockheed Martin, USA 

Manuel Lopes, INRIA, France 

Gonzalo Lopez-Nicolas, Univ. Zaragoza, Spain 

Yunjiang Lou, Harbin Inst. of Tech., China 

Ou Ma, New Mexico State Univ., USA 

Shugen Ma, Ritsumeikan Univ., Japan 

Yusuke Maeda, Yokohama National Univ., Japan 

Mohammad Mahoor, Denver Univ., USA 

Eric Marchand, INRIA Rennes, France 

Gian Luca Mariottini, Univ. Texas Arlington, USA 

Agostino Martinelli, INRIA Rhone-Alpes, France 

Philippe Martinet, Blaise Pascal Univ., France 

Alcherio Martinoli, EPFL, Switzerland 

Favio Masson, Univ. Nacional del Sur, Argentina 

Fumitoshi Matsuno, Kyoto Univ., Japan 

Claudio Melchiorri, Univ. Bologna, Italy 

Arianna Menciassi, Scuola Sup. Sant'Anna, Italy 

Giorgio Metta, IIT, Italy 

Javier Minguez, Univ. Zaragoza, Spain 

Sarthak Misra, Univ. Twente, Netherlands 

Mark Moll, Rice Univ., USA 

Katja Mombaur, Univ. Heidelberg, Germany 

Hyungpil Moon, Sungkyunkwan Univ., Korea 

Antonio Morales, Univ. Jaume I, Spain 

Marco Morales, Inst. Tecn. Autónomo, Mexico 

Taketoshi Mori, Univ. Tokyo, Japan 

Jun Morimoto, ATR, Japan 

El Mustapha Mouaddib, Univ. of Picardie, France 

Andreas Mueller, Univ. Duisburg-Essen, Germany 

Hiroki Murakami, Ishikawajima-Harima Ind., Japan 

Rafael Murrieta-Cid, CIMAT, Mexico 

Fawzi Nashashibi, INRIA, France 

Jose Neira, Univ. Zaragoza, Spain 

Jason O'Kane, Univ. of South Carolina, USA 

Tetsuya Ogata, Kyoto Univ., Japan 

Edwin Olson, Univ. of Michigan, USA 

Giuseppe Oriolo, Univ. Roma “La Sapienza”, Italy 

Koichi Osuka, Osaka Univ., Japan 

Erika Ottaviano, Univ. Cassino, Italy 

Erhan Oztop, ATR/NICT, Japan 

Taskin Padir, WPI, USA 

Evangelos Papadopoulos, NTU Athens, Greece 

Jong Hyeon Park, Hanyang Univ., Korea 

Ioannis Pavlidis, Univ. Houston, USA 

Janice Pearce, Berea College, USA 

Angelika Peer, TU Munchen, Germany 

Jan Peters, Max Planck Inst., Germany

Thierry Peynot, Univ. Sydney, Australia 

Erion Plaku, Catholic Univ. of America, USA 

Erwin Prassler, Hoch. Bonn-Rein-Sieg, Germany 

Joerg Raczkowsky, Univ. Karlsruhe, Germany 

Subramanian Ramamoorthy, Univ. Edinburgh, UK 

Yiannis Rekleitis, McGill Univ., Canada 

Patrick Rives, INRIA, France 

Cameron Riviere, CMU, USA 

Paolo Robuffo Giordano, Max Planck Inst., Germany 

Paolo Rocco, Politecnico di Milano, Italy 

Raul Rojas, Freie Universität Berlin, Germany 

Radu Bogdan Rusu, Willow Garage Inc., USA 

Paul Rybski, Carnegie Mellon Univ., USA 

Jee-Hwan Ryu, Korea Univ. Tech. & Edu., Korea 

Carlos Sagues, Univ. Zaragoza, Spain 

Srikanth Saripalli, Arizona State Univ., USA 

Paul Scerri, Carnegie Mellon Univ., USA 

Jim Schmiedeler, Univ. of Notre Dame, USA 

Wei-Min Shen, USC, USA 

Yantao Shen, Univ. of Nevada, Reno, USA 

Anton Shiriaev, Umea Univ., Sweden 

Thierry Simeon, LAAS-CNRS, France 

Reid Simmons, CMU, USA 

Metin Sitti, CMU, USA 

Ryan Smith, Queensland Univ. of Tech., Australia 

Jorge Solis, Waseda Univ., Japan 

Jae-Bok Song, Korea Univ., Korea 

Mohan Sridharan, Texas Tech, USA 

Siddhartha Srinivasa, Intel Labs, USA 

Cyrill Stachniss, Univ. Freiburg, Germany 

Mike Stilman, Georgia Tech, USA 

Kasper Stoy, Univ of Southern Denmark, Denmark 

Ethan Stump, ARL, USA 

Jianbo Su, Shanghai Jiao Tong Univ., China 

Attawith Sudsang, Chulalongkorn Univ., Thailand 

Il Hong Suh, Hanyang Univ., Korea 

Salah Sukkarieh, Univ. Sydney, Australia 

Dong Sun, City Univ. Hong Kong, China 

Yu Sun, Univ. Toronto, Canada 


–vii– 

Yukio Takeda, Tokyo Inst. of Technology, Japan 

Jindong Tan, MTU, USA 

Fang Tang, California State Polytech. Pomona, USA 

Bert Tanner, Univ. of Delaware, USA 

Adriana Tapus, ParisTech, France 

Juan Tardos, Univ. Zaragoza, Spain 

Ashley Tews, CSIRO, Australia 

Marc Toussaint, TU Berlin, Germany 

Jeff Trinkle, Rensselaer Polytechnic Inst., USA 

Nikolaos Tsagarakis, IIT, Italy 

Ales Ude, Jozef Stefan Inst., Slovenia 

Jun Ueda, Georgia Tech, USA 

Kazunori Umeda, Chuo Univ., Japan 

Pascal Vasseur, Univ. Rouen, France 

Manuela Veloso, CMU, USA 

Marilena Vendittelli, Univ. Roma “La Sapienza”, Italy 

Markus Vincze, Vienna Univ. of Tech., Austria 

Marsette Vona, Northeastern Univ., USA 

Richard Voyles, Denver Univ., USA 

Danwei Wang, Nanyang Techn. Univ., Singapore 

Fei-Yue Wang, Univ. of Arizona, USA 

Hesheng Wang, Shanghai Jiao Tong Univ., China 

Stefan Williams, Univ. Sydney, Australia 

Reg Wilson, JPL, USA 

Denis Wolf, Univ. Sao Paulo, Brazil 

Fengfeng Xi, Ryerson Univ., Canada 

Jing Xiao, UNCC, USA 

Yasushi Yagi, Osaka Univ., Japan 

Katsu Yamane, Disney Research, USA 

Guilin Yang, Inst. of Manuf. Tech., Singapore 

Anna Yershova, Duke Univ., USA 

Jingang Yi, Rutgers, USA 

Eiichi Yoshida, AIST, Japan 

Takashi Yoshimi, Shibaura Inst. of Tech., Japan 

Peijang Yuan, Beihang Univ., China 

Fumin Zhang, Georgia Tech, USA 

Hong Zhang, Univ. of Alberta, Canada 

Jiang-Yu Zheng, IUPUI, USA

IROS 2011 Reviewers 

Joel Abadie 

Gursel Alici 

Bilal Ahmed Arain 

Jose Baca 

Jake Abbott 

Hatem Alismail 

Nancy Arana-Daniel Abraham Bachrach 

Tariq Abdelrahman 

Ben Allan 

Joan Aranda 

Wael Bachta 

Mohamed Abderrahim Peter Allen 

Miguel Aranda 

Leonardo Badino 

Hamid Abdi 

River Allen 

Jumpei Arata 

Norm Badler 

Nichola Abdo 

Guillaume Allibert 

Helder Araujo 

Ji-Hun Bae 

Farzaneh Abdollahi 

James Allington 

Rui Araújo 

Joonbum Bae 

A. H. Abdul Hafez 

Benedetto Allotta 

Mario Arbulu 

Stanley Baek 

Alexandre Abellard 

Jose Almeida 

Laurent Arcese 

Mitra Bahadorian 

Leon Abelmann 

Luis Almeida 

James Archibald 

Alexander Bahr 

Pramod Abichandani Oscar Alonso 

Gustavo Arechavaleta He Bai 

Satoko Abiko 

Redwan Alqasemi 

Juan Carlos Arevalo Qadeer Baig 

Elias Abou Zeid 

Pablo Javier Alsina 

Adrian Arfire 

Tim Bailey 

Konstantinos Aboudolas Ron Alterovitz 

Brenna Argall 

John Baillieul 

Dino Accoto 

Salah Althloothi 

Sylvain Argentieri 

Andrea Bajo 

Jeffrey Ackerman 

Kaspar Althoefer 

Miguel Arias 

Max Bajracharya 

Carlos A. Acosta Calderon Daniel Althoff 

Hiroaki Arie 

Christopher R Baker 

Martin Adams 

Armando Alves Neto Keisuke Arikawa 

Benjamin Balaguer 

Antonio Adan 

Angelos Amanatiadis Masakazu Arima 

Carlos Balaguer 

Olaoluwa Adeniba 

Yoshiharu Amano 

Hichem Arioui 

Stephen Balakirsky 

Nagesh Adluru 

Josep Amat 

Christopher Armbrust Ravi Balasubramanian 

Lounis Adouane 

Nancy M. Amato 

Leopoldo Armesto 

Sivakumar Balasubramanian 

Erwin Aertbelien 

Cinzia Amici 

Jose Armingol 

Tucker Balch 

Zohaib Aftab 

Luis Ernesto Amigo 

Kai Oliver Arras 

Devin Balkcom 

Gabriel Agamennoni Francesco Amigoni 

Filippo Arrichiello 

Dana Ballard 

Priyanshu Agarwal 

Vahid Aminzadeh 

Marc Arsenault 

Hansjorg Baltes 

Ali-akbar Agha-mohammadi Farshid Amirabdollahian Marc Arsicault 

Haris Baltzakis 

Navid Aghasadeghi 

Mehdi Ammi 

Panagiotis Artemiadis Xiaojun Ban 

Mahdi Agheli 

Arvind Ananthanarayanan Jordi Artigas 

Jared Bancroft 

Farhad Aghili 

Margarita Anastassova Minoru Asada 

Antonio Bandera 

Noa Agmon 

Michael Skipper Andersen Ali Asadian 

Antonio Bandera Rubio 

Lucio Agostinho Rocha John Anderson 

Masoud Asadpour 

Juan Pedro Bandera Rubio 

Amer Agovic 

Monica Anderson 

Hajime Asama 

Tirthankar Bandyopadhyay 

Gabriel Aguirre-Ollinger Sterling Anderson 

Fumihiko Asano 

Mayank Bansal 

Carl Ahlberg 

Stuart Anderson 

Tamim Asfour 

Emilia I. Barakova 

Aamir Ahmad 

Sean Andersson 

Lars Asplund 

Adrien Baranes 

Mojtaba Ahmadi 

Franz Andert 

Seyed Farokh Atashzar Jose Barata 

Hossein Ahmadzadeh Takeshi Ando 

J. Alan Atherton 

Timothy Barfoot 

Nisar Ahmed 

Juan Andrade-Cetto Christopher Atkeson Stephen Barkby 

Sang Chul Ahn 

Henrik Andreasson 

Jason Atkin 

Adrien Barral 

SungHwan Ahn 

Nicolas Andreff 

Samuel Au 

Alejandra Barrera 

Omar Ait-Aider 

Alexander Andreopoulos Tsz-Chiu Au 

João P. Barreto 

Yasumichi Aiyama 

Claude Andriot 

Fernando Auat Cheein David Barrett 

Batu Akan 

Wei Tech Ang 

Pierre Avanzini 

Antonio Barrientos 

Adel Akbarimajd 

Jorge Angeles 

J. Gabriel Avina-Cervantes Jordi Barrio Gragera 

Abdullah Akce 

Adrien Angeli 

Carlo Alberto Avizzano Luis J. Barrios 

Erhan Akdogan 

Andreas Angerer 

Kean C. Aw 

Adrien Bartoli 

Srinivas Akella 

David A. Anisi 

Ben Axelrod 

Luca Bascetta 

Baris Akgun 

Roberta Annicchiarico Mustafa Ayad 

Cagatay Basdogan 

Aadeel Akhtar 

David Miguel Antunes Victor Ayala-Ramirez Nicola Basilico 

H. Levent Akin 

Salvatore Maria Anzalone Nora Ayanian 

Maxim Batalin 

Yoshitake Akiyama 

Shinya Aoi 

Olivier Aycard 

Adam Bates 

Samer Al Moubayed Takeshi Aoki 

Alper Aydemir 

Jorge Batista 

Sven Albrecht 

Yannick Aoustin 

Pedram Azad 

Gabriel Baud-Bovy 

Alin Albu-Schäffer 

Tadayoshi Aoyama 

Christine Azevedo Coste Marcus Baum 

Javier Adolfo Alcazar Sumeet S. Aphale 

Asma Azim 

Eric Baumgartner 

Feras Alchama 

Ilias Apostolopoulos Jose Raul Azinheira Enrique Bauzano 

David Aldavert 

Rosario Aragues 

Mahdi Azizian 

Eduardo-J. Bayro-Corrochano 

Alen Alempijevic 

Fumihito Arai 

Jose M. Azorin 

Matthew Bays 

Guillem Alenyà 

Takeshi Arai 

Y. Babazadeh Bedoustani Cabbar V. Baysal 

Jacopo Aleotti 

Tatsuo Arai 

Jan Babic 

Ali Bazaei 


–viii–

Stephane Bazeille 

Jean-Charles Bazin 

Chris Beall 

Ozkan Bebek 

Israel Becerra 

Aaron Becker 

Brian C. Becker 

Jan Becker 

Marcelo Becker 

Randall Beer 

Kristopher Beevers 

Jounghoon Beh 

Aman Behal 

Evan Behar 

Laxmidhar Behera 

Michael Behrens 

Roland Behrens 

Maximilian Beinhofer 

Kostas E. Bekris 

Anna Belardinelli 

Rachid Belaroussi 

Karim Belharet 

Martin Bello 

Nicola Bellotto 

Tony Belpaeme 

Faiz Ben Amar 

Heni Ben Amor 

Fathi Ben Ouezdou 

Pinhas Ben-Tzvi 

Mehdi Benallegue 

Asher Bender 

Rodrigo Benenson 

Ben Benfold 

Beno Benhabib 

Mohamed Bensalah 

Darrin Bentivegna 

Ahmed Benzerrouk 

Dmitry Berenson 

Massimo Bergamasco 

Luis Miguel Bergasa 

Sarah Bergbreiter 

Christos Bergeles 

Cyrille Berger 

Marcel Bergerman 

Peter Berkelman 

Spring Berman 

Alexandre Bernardino 

Karsten Berns 

Christian Bersch 

Giovanni Berselli 

Jean-Marc Berthommé 

Sylvain Bertrand 

Eva Besada-Portas 

Ross Beveridge 

David Bevly 

Felix Beyeler 

Jürgen Beyerer 

Nicola Bezzo 

Amit Bhatia 

Rajankumar Bhatt 

Sourabh Bhattacharya 

Subhrajit Bhattacharya 

Luigi Biagiotti 

Joshua J Bialkowski 

Antonio Bicchi 

Manuele Bicego 

Estela Bicho 

Philippe Bidaud 

Julien Bidot 

Alexander Bierbaum 

Geoffrey Biggs 

Hakan Bilen 

Aude Billard 

Ghassan Bin Hammam 

Jonathan Binney 

Oliver Birbach 

Stan Birchfield 

Nathaniel Bird 

Andreas Birk 

Rachael Bis 

Horst Bischof 

Bradley Bishop 

Hannes Bistry 

Joydeep Biswas 

Sebastian Bitzer 

Lars Blackmore 

François Blais 

Jose-Luis Blanco 

M. Dolores Blanco 

Sebastian Blumenthal 

Antônio Padilha Lanari Bó 

Michael Boardman 

Botond Bocsi 

Jeannette Bohg 

Aude Bolopion 

Vanderlei Bonato 

Adrian Bonchis 

Gary Bone 

Ilian Bonev 

Marcello Bonfe 

Silvere Bonnabel 

Devin Bonnie 

Philippe Bonnifait 

Wayne Book 

Shaunak D. Bopardikar 

Mirko Bordignon 

Johann Borenstein 

Paulo Vinicius K. Borges 

Gianni Borghesan 

Per Henrik Borgstrom 

Ali Borji 

Branislav Borovac 

Michael Bosse 

Silvia Botelho 

Debora Botturi 

Samir Bouabdallah 

Patrick Michael Bouffard 

Abdelbaki Bouguerra 

Mehdi Boukallel 

Abdeslam Boularias 

Mohamed Bouri 

Karim Bouyarmane 

Alan Bowling 

Isil Bozma 

David Bradley 

David Bradley 

Gary Bradski 

Julia Braman 


–ix– 

Alexandre Santos Brandao 

David Brandt 

Michael Branicky 

Kusy Brano 

David J. Braun 

Cynthia Breazeal 

Sebastian Brechtel 

Andreas Breitenmoser 

Michael Brenner 

Jose Breñosa 

Timothy Bretl 

Reuben Brewer 

Adrien Briod 

Sébastien Briot 

Oliver Brock 

Roland Brockers 

Yury Brodskiy 

Graham Brooker 

Christopher Brooks 

Jonathan Brookshire 

Xavier Broquere 

H. Ben Brown 

Travis Brown 

Brett Browning 

Frank Broz 

Drazen Brscic 

James Robert Bruce 

Davide Brugali 

Alberto Brunete 

Emma Brunskill 

Herman Bruyninckx 

Adam Bry 

Mitch Bryson 

Dirk Buchholz 

Jonas Buchli 

Jennifer Buehler 

Heiko Buelow 

Jose M. Buenaposada 

Olivier Buffet 

Magdalena Bugajska 

Chris Burbridge 

Wolfram Burgard 

Xavier Burgos-Artizzu 

Antoni Burguera 

Arne Burisch 

Jenny Burke 

Corves Burkhard 

Darius Burschka 

Thomas Buschmann 

Antoine Bussy 

Evan Butler 

Zack Butler 

Jonathan Butzke 

Georg Bätz 

Berthold Bäuml 

Fernando Caballero 

Flavio Cabrera-Mora 

Gonçalo Cabrita 

Fabrizio Caccavale 

Cesar Dario Cadena Lerma 

Binghuang Cai 

Hongyuan Cai 

Yueri Cai 

Andrea Caiti 

Maya Cakmak 

Andrea Calanca 

Darwin G. Caldwell 

Timothy Caldwell 

Daniele Caligiore 

Sylvain Calinon 

Daniele Calisi 

Massimo Callegari 

Andrew Calway 

Stephen Cameron 

Jason Campbell 

Mark Campbell 

Domenico Campolo 

Jordi Campos 

Mario Montenegro Campos 

Salvatore Candido 

Angelo Cangelosi 

Giorgio Cannata 

Jiangtao Cao 

Jesus Capitan 

David Cappelleri 

Barbara Caputo 

Alexander Carballo 

Giuseppe Carbone 

Philippe Cardou 

Ricardo Carelli 

Anthony Carfang 

Craig Carignan 

Christophe Cariou 

Nicolas Carlési 

Luca Carlone 

Raffaella Carloni 

Tom Carlson 

Stéphane Caro 

Guillaume Caron 

Stefano Carpin 

Giorgio Carpino 

Marc Carreras 

Juan A. Carretero 

Henry Carrillo 

J. Carlos Mendes Carvalho 

Maura Casadio 

Giuseppe Casalino 

Gianni Castelli 

Virginia Castelli 

Claudio Castellini 

Pedro Castillo 

Eduardo Castillo-Castaneda 

Modesto Castrillón 

Glauco A. de Paula Caurin 

Albert Causo 

Adriano Cavalcanti 

Filippo Cavallo 

Gary Cavanough 

M. Cenk Cavusoglu 

Miguel Cazorla 

Enric Celaya 

Zhiwei Cen 

Andrea Censi 

Pablo Cerrada 

Enric Cervera 

Damien Chablat 

Marco A. Chacin Torres 

Brahim Chaib-draa

Nicolas Chaillet 

Luiz Chaimowicz 

Nilanjan Chakraborty 

Punarjay Chakravarty 

Suman Chakravorty 

Hugo Chale-Gongora 

Maxime Chalon 

Lyle Chamberlain 

Thierry Chaminade 

Ambrose Chan 

Chen-Yu Chan 

Mervin Chandrapal 

Doyoung Chang 

Jen-Yuan (James) Chang 

Lillian Chang 

Richard Chang 

Yung-Jung Chang 

Crystal Chao 

Roland Chapuis 

Jean-Remy Chardonnet 

Marcela Charfuelan 

Francois Charpillet 

Thierry Chateau 

Dimitris Chatzigeorgiou 

Pratik Chaudhari 

Francois Chaumette 

Ricardo O. Chavez Garcia 

C. C. Cheah 

Paul Checchin 

Denis Chekhlov 

Ryad Chellali 

Ahmed Chemori 

Baojun Chen 

Chao Chen 

Haoyao Chen 

Heping Chen 

Hongjun Chen 

Ian Yen-Hung Chen 

Jian Chen 

Qijun Chen 

Qing Chen 

Weidong Chen 

Weihai Chen 

Wenjie Chen 

Wenjie Chen 

Xin Chen 

Xin Chen 

Yongquan Chen 

Yuren Chen 

Zhaopeng Chen 

Zheng Chen 

Zhiyong Chen 

Gordon Cheng 

Harry Cheng 

Victor H.L. Cheng 

Yang Cheng 

Joono Cheong 

Véronique Cherfaoui 

Anoop Cherian 

Sonia Chernova 

Andrea Cherubini 

Graziano Chesi 

Mohamed Chetouani 

Christine Chevallereau 

Chee Meng Chew 

Héctor Chiacchiarini 

Mu Chiao 

Ryosuke Chiba 

Cheng Chin 

Eris Chinellato 

Francesco Chinello 

Kiyoyuki Chinzei 

Gregory Chirikjian 

Hamidreza Chitsaz 

Han-Pang Chiu 

Margarita Chli 

Baek-Kyu Cho 

Jang Ho Cho 

Kyu-Jin Cho 

Han-Lim Choi 

Hee-Byoung Choi 

Jeong-Sik Choi 

Young-ho Choi 

Youngjin Choi 

Nikhil Chopra 

Safwan Choudhury 

Anders Lyhne Christensen 

David Johan Christensen 

Henrik Iskov Christensen 

Anna-Karin Christiansson 

Eftychios Christoforou 

Kang-Ching Chu 

Daisuke Chugo 

Changmook Chun 

Timothy H. Chung 

Wan Kyun Chung 

Matteo Cianchetti 

Anna Lisa Ciancio 

Grzegorz Cielniak 

Matei Ciocarlie 

Christian Cipriani 

Xavier Clady 

Mark Claffee 

Christopher M. Clark 

James Clark 

Jonathan Clark 

José Claver 

Kevin Cleary 

Charles Clercq 

Cédric Clévy 

Salvador Cobos Guzman 

José M. Cogollor Delgado 

Benjamin Cogrel 

Benjamin Cohen 

Paul Cohen 

A. Paulo Coimbra 

Michael Coleman 

Edward Colgate 

Alvaro Collet 

Christophe Collewet 

Roberto Colombo 

Julian Colorado 

Brian Coltin 

Frederic Comby 

Olivier Company 

Andrew Ian Comport 

Michele Conconi 

Shuang Cong 


–x– 

David Conner 

Christian Pascal Connette 

Jorg Conradt 

Daniela Constantinescu 

Francois Conti 

Marco Controzzi 

Atlas F. Cook IV 

Joseph Cooper 

Jeremy Cooperstock 

Lennon Cork 

Peter Corke 

Alex Cornejo 

Jordi Cornella 

Nikolaus Correll 

Nick Corson 

Rui Cortesao 

Anna H. R. Costa 

Joao Paulo Costeira 

Sebastien Cotton 

Estelle Courtial 

Noah J. Cowan 

Anthony Cowley 

Eric Coyle 

John Crassidis 

Alessandro Crespi 

Vincent Creuze 

Vincent Crocher 

André Crosnier 

Charles R. Crowell 

Xavier Cufi 

Jinshi Cui 

Lei Cui 

Yanzhe Cui 

Mark Joseph Cummins 

Alexander Cunningham 

Robert Cupec 

Mark Cutkosky 

Filippo D'Ippolito 

Boubaker Daachi 

Torbjorn Dahl 

Christian Dahmen 

Xiaochen Dai 

Matthew N. Dailey 

Konstantinos Dalamagkidis 

Sebastien Dalibard 

Fabio DallaLibera 

Skyler Dalley 

Mohsen Dalvand 

Bruno Damas 

Patrick Danès 

Hao Dang 

Kostas Daniilidis 

Todd Danko 

Neil Dantam 

Thanh-Son Dao 

Behzad Dariush 

Colin Das 

Jnaneshwar Das 

Raj Dasgupta 

Hisashi Date 

Chandan Datta 

Philip David 

Duane Davis 

James Davis 

Andrew J Davison 

Alireza Davoodi 

Shameka Dawson 

Feras Dayoub 

Anibal de Almeida 

Raoul de Charette 

Jesús M de la Cruz 

Arturo de la Escalera 

Tinne De Laet 

Kathryn De Laurentis 

Alessandro De Luca 

Giuseppe De Maria 

A. A. de Menezes Pereira 

Elena De Momi 

Agostino De Santis 

Michael Defoort 

Sarah Degallier 

Amir Degani 

Ehsan Dehghan 

Marc Peter Deisenroth 

Jaime del Cerro 

Frédéric Delaunay 

Anne Delettre 

Frank Dellaert 

Babette Dellen 

Vivien Delsart 

Eric Demeester 

Emel Demircan 

Yiannis Demiris 

Cédric Demonceaux 

Oscar Déniz 

Jory Denny 

Anna Derbakova 

Jason Derenick 

Konstantinos Dermitzakis 

Ashish Deshpande 

Guilherme DeSouza 

Renaud Detry 

Carrick Detweiler 

Michel Devy 

Debadeepta Dey 

Travis Deyle 

Ezequiel Di Mario 

Rosen Diankov 

Xiumin Diao 

Jorge Dias 

M. Bernardine Dias 

M. Freddie Dias 

Iñaki Díaz 

James Diaz 

Jakob Lund Dideriksen 

Alexander Dietrich 

Charles Dietrich 

Michael Dille 

Eric D. Diller 

Rüdiger Dillmann 

Edward Dillon 

Haris Dindo 

Feng Ding 

HuaFeng Ding 

Liang Ding 

Xilun Ding 

Xu Chu Ding 

Thang Dinh

Albert Diosi 

Gamini Dissanayake 

Warren Dixon 

Vladimir Djapic 

Dalila Djoudi 

Martin Do 

Phuong Anh Do Hoang 

Andrew Dobson 

Zachary Dodds 

Mehmet Remzi Dogar 

Nakju Doh 

Miwato Doi 

Christophe Doignon 

John M. Dolan 

Dmitri Dolgov 

Mihai Emanuel Dolha 

Irina Dolinskaya 

Aaron Dollar 

Etienne Dombre 

Paolo Domenici 

Peter Ford Dominey 

Haiwei Dong 

Hao Dong 

Jun Feng Dong 

Lixin Dong 

Shuonan Dong 

Wenjie Dong 

Strahinja Dosen 

Finale Doshi 

Bertrand Douillard 

Zoe Doulgeri 

Anca Dragan 

Andrew Drenner 

Paulo Drews Jr 

Evan Drumwright 

Sebastien Druon 

Haiping Du 

Noel E. Du Toit 

Steven Dubowsky 

Tom Duckett 

Gregory Dudek 

Elliot Duff 

Matthew David Dunbabin 

Claire Dune 

Matthew Dunnigan 

Tuan Duong 

Edmond DuPont 

Pierre Dupont 

Francesco Durante 

Christian Duriez 

Jason Durrie 

Alexander Duschau-Wicke 

Ethan Eade 

Matthew Earl 

Aaron Edsinger 

Magnus Egerstedt 

Ruwan Egoda Gamage 

Amy Eguchi 

Donald Eickstedt 

Robert Eidenberger 

Carl Henrik Ek 

Chinwe Ekenna 

Oussama El Hamzoui 

Sahar El Khoury 

Ahmed K. El-Shenawy 

Imad Elhajj 

Haytham Elhawary 

Robert Ellenberg 

Jan Elseberg 

Jack Elston 

Phillips Emilie 

Gen Endo 

Takahiro Endo 

Felix Endres 

Erik Daniel Engeberg 

Brendan Englot 

Clemens Eppner 

Alina M. Eqtami 

Kemalettin Erbatur 

Tom Erez 

Lawrence H Erickson 

Stefan Ericson 

Gorkem Erinc 

Aydan Erkmen 

Ismet Erkmen 

Duygun Erol Barkana 

Adrien Escande 

Bernard Espiau 

Joel Esposito 

Claudia Esteves 

Carlos Estrada 

Simos Evangelou 

William C. Evans 

Jani Even 

Jacob Everist 

Friederike Eyssel 

Stephan Fabel 

Tibor Fabian 

Andrew Fagg 

Farbod Fahimi 

Nathaniel Fairfield 

Pietro Falco 

Riccardo Falconi 

Maurice Fallon 

Egidio Falotico 

Yongchun Fang 

Pietro Fanghella 

Isabelle Fantoni 

Gregory Faraut 

Alessandro Farinelli 

Ildar Farkhatdinov 

Shane Farritor 

Imraan Faruque 

Juan Fasola 

John Fasoulas 

Sergej Fatikow 

Abbas Fattah 

Ehsan Fazl-Ersi 

Pooyan Fazli 

Ronald Fearing 

Duc Fehr 

David Feil-Seifer 

Javier Felip 

Martin Felis 

Mirko Felisa 

Vicente Feliu 

Aaron Fenster 

Cornelia Fermuller 


–xi– 

Leandro Carlos Fernandes 

Eduardo Fernandez 

P. Fernández Alcantarilla 

M. Fernandez-Carmona 

Jesus Fernandez-Lozano 

J.-A. Fernandez-Madrigal 

C. Fernandez-Maloigne 

Vincenzo Ferrari 

Manuel Ferre 

Alexander Ferrein 

Antoine Ferreira 

João Filipe Ferreira 

Manuel Ferreira 

Gonzalo Ferrer 

Gianni Ferretti 

Giancarlo Ferrigno 

António Ferrolho 

Mark Fiala 

Gabor Fichtinger 

MaryAnne Fields 

Rafael Fierro 

Giorgio Figliolini 

Ioannis Filippidis 

David Filliat 

Holger Finger 

Allan Daniel Finistauri 

Jonathan Fink 

Bernd Finkemeyer 

Paolo Fiorini 

Gregory Fischer 

Jan Fischer 

Wolfgang Fischer 

Robert Charles Fitch 

Kevin Fite 

Fabrizio Flacco 

David Flavigné 

Daniel Montrallo Flickinger 

Dario Floreano 

Mihaela Cecilia Florescu 

Michele Focchi 

David Fofi 

Torea Foissotte 

Amalia Foka 

David Folio 

John Folkesson 

Romeo Fomena Tatsambon 

Josep Maria Font-Llagunes 

Marco Fontana 

Daniele Fontanelli 

James Richard Forbes 

Amir Ali Forough Nassiraei 

Per-Erik Forssen 

Clément Fouque 

Michael Fox 

Jan-Michael Frahm 

Juan-Carlos Fraile 

Philippe Fraisse 

Antonio Franchi 

Barbara Frank 

Heinz Frank 

Jörg Franke 

Michel Franken 

Friedrich Fraundorfer 

Emilio Frazzoli 

Luigi Freda 

Regina Frei 

Leonid Freidovich 

Vincent Fremont 

Udo Frese 

Eric W. Frew 

Georges Fried 

Werner Friedl 

Martin Friedmann 

Simone Frintrop 

Mario Fritz 

Anselmo Frizera Neto 

Emanuele Frontoni 

Dominic R. Frutiger 

Li-Chen Fu 

Michael J. Fu 

Karl Cheng-Heng Fua 

Ohmi Fuchiwaki 

Masakatsu G. Fujie 

Hideo Fujimoto 

Kikuo Fujimura 

Toshihiro Fujita 

Takanori Fukao 

Toshio Fukuda 

Kotaro Fukui 

Rui Fukui 

Yasuhiro Fukuoka 

Edwardo F. Fukushima 

Yosuke Fukushima 

Matteo Fumagalli 

Ryu Funase 

Tetsuro Funato 

Juan G. Victores 

Péter Galambos 

Ignacio Galiana 

Cipriano Galindo 

Juan Alvaro Gallego 

Kevin Galloway 

Stefano Galvan 

Dorian Galvez Lopez 

Javier Gamez Garcia 

Andrej Gams 

Dongming Gan 

Seng Keat Gan 

Jacques Gangloff 

Bingtuan Gao 

Haibo Gao 

Elena Garcia 

Fernando Garcia 

Pablo Garcia 

Rafael Garcia 

Sebastian García 

Alfonso García-Cerezo 

Simon Garnier 

Carlos Garre 

Anais Garrell 

Jose Gaspar 

Alessandro Gasparetto 

Andrea Gasparri 

Roger Gassert 

Philippe Gaussier 

Michael Gauthier 

Russell Gayle 

Yunjian Ge

Anne-Lise Gehin 

Christopher Geib 

Andreas Geiger 

Peter Gemeiner 

Sebastien Gemme 

Michael Gennert 

Brian Gerkey 

Gregory J. Gerling 

Sven Gestegård Robertz 

Hartmut Geyer 

Robert Ghrist 

Michael Gienger 

Philippe Giguere 

Arturo Gil 

Stephanie Gil 

Jeremy Gillula 

Antonio Gimenez 

Luca Giona 

Xavier Giralt 

Nivedhitha Giri 

Dylan F. Glas 

Sebastien Glaser 

Guy Godin 

Roy Godzdanker 

Robert Goeddel 

Christian Goerick 

Martin Goerner 

Grigore Gogu 

Orcun Goksel 

Ken Goldberg 

Michael Goldfarb 

Fernando Gomez 

A. Gómez de Silva Garza 

Nelson Goncalves 

Paulo Gonçalves 

Tiago Gonçalves 

Javier Gonzalez 

Juan Pablo Gonzalez 

Víctor González 

Yolanda Gonzalez 

Luis E. González-Jiménez 

Bill Goodwine 

Faramarz Gordaninejad 

Francisco Gordillo 

Clement Gosselin 

Florian Gosselin 

Frederick P. Gosselin 

Ambarish Goswami 

Dip Goswami 

Michèle Gouiffès 

Stephen Gould 

François Goulette 

Marc Gouttefarde 

Sven Gowal 

Nuno Gracias 

Devin Grady 

Birgit Graf 

Christophe Grand 

Grzegorz Granosik 

Oscar G. Grasa 

Valdir Grassi Junior 

Antoni Grau 

Jan Tommy Gravdahl 

David Gravel 

Andrew Gray 

Steven Gray 

Markus Grebenstein 

Colin Green 

Keith Evan Green 

Robert D. Gregg 

Nicola Greggio 

Lorenzo Grespan 

Matthew Greytak 

Elena Gribovskaya 

Matthew Koichi Grimes 

Garrett Grindle 

Giorgio Grioli 

Ben Grocholsky 

Martin Groeger 

Daniel Grollman 

William Grosky 

Roderich Gross 

Thilo Grundmann 

Rod Grupen 

Slawomir Grzonka 

Jason Gu 

Yisheng Guan 

Corrado Guarino Lo Bianco 

Wail Gueaieb 

Eugenio Guglielmelli 

Mohamed Guiatni 

Jose Guivant 

James Gunderson 

Keith Gunura 

Chunzhao Guo 

Shuxiang Guo 

Yan Guo 

Yi Guo 

Rakesh Gupta 

Sabri Gurbuz 

Hakan Gurocak 

Jens-Steffen Gutmann 

Stephen J. Guy 

Felix Hackbarth 

Yassine Haddab 

Amir Haddadi 

Sami Haddadin 

Hicham Hadj-Abdelkader 

Testuji Haga 

Nicola Hagemeister 

Gregory Hager 

Norihiro Hagita 

Masaya Hagiwara 

Tamas Haidegger 

Adam Halasz 

Dogan Sinan Haliyo 

Aarne J. Halme 

Dan Halperin 

Masaki Hamamoto 

Tarek Hamel 

William R. Hamel 

Chang-Soo Han 

Gyu Chull Han 

Jianda Han 

Kyung min Han 

Li Han 

Long Han 

Ryo Hanai 


–xii– 

Ankur Handa 

Uwe D. Hanebeck 

Guangbo Hao 

Masayuki Hara 

Naoyuki Hara 

Susumu Hara 

Kanako Harada 

Kensuke Harada 

Takashi Harada 

Tatsuya Harada 

Frederick Harris 

Stephen Hart 

Robert Haschke 

Osamu Hasegawa 

Tadahiro Hasegawa 

Tsutomu Hasegawa 

Yasuhisa Hasegawa 

Kenji Hashimoto 

Koichi Hashimoto 

Minoru Hashimoto 

Shuji Hashimoto 

Takuya Hashimoto 

Naohisa Hashimoto 

Takeshi Hatanaka 

Naotaka Hatao 

Ross Hatton 

Sabine Hauert 

Helmut Hauser 

Kris Hauser 

Nicolas Hautière 

Nick Hawes 

Yoshikazu Hayakawa 

Mitsuhiro Hayashibe 

Jean-Bernard Hayet 

Galen Clark Haynes 

Vincent Hayward 

Klas Hedenberg 

Markus Hehn 

Hordur K Heidarsson 

Björn Hein 

Milton Heinen 

Achim Hekler 

Patrick Helmer 

Gustaf Hendeby 

Gregory Henderson 

Thomas C. Henderson 

Philipp Hennig 

Peter Henry 

Damitha Chandana Herath 

Evan Herbst 

Just Herder 

Andrei Herdt 

Bruno Herisse 

Sorin Herle 

Benoît Herman 

Alejandro Hernandez Arieta 

Daniel Hernández-Sosa 

Albert Hernansanz 

Micha Hersch 

Joachim Hertzberg 

Joel Hesch 

Tarcisio Hess-Coelho 

Matthew Scott Hester 

Todd Hester 

Clint Heyer 

Mitsuru Higashimori 

Masaru Higuchi 

Ulrich Hillenbrand 

Nick Hillier 

Volker Hilsenstein 

Salim Hima 

Lindsey Hines 

James Hing 

Tatsuya Hirahara 

Hiroaki Hirai 

Yasuharu Hirasawa 

Yasuhisa Hirata 

Heiko Hirschmüller 

Gerd Hirzinger 

Handa Hisashi 

Sean Ho 

Van Ho 

John Hoare 

Warren Hoburg 

Nico Hochgeschwender 

Antony Hodgson 

Michael Hofbaur 

Nicholas Hoff 

Alwin Hoffmann 

Gabriel Hoffmann 

Heiko Hoffmann 

Andrew Hogue 

Shinji Hokamoto 

Geoffrey Hollinger 

Patrick Hollis 

Rebecca Hollmann 

Dirk Holz 

Masaaki Honda 

Man Bok Hong 

Carl D. Hoover 

Ben Horan 

Toshio Hori 

Armin Hornung 

Satoshi Hoshino 

Koh Hosoda 

Shigeyuki Hosoe 

Feili Hou 

Jonathan How 

Ayanna Howard 

Matthew Howard 

Tom Howard 

Robert D. Howe 

Stefan Hrabar 

Kaijen Hsiao 

M. Ani Hsieh 

David Hsu 

John Hsu 

Bo Hu 

Chao Hu 

Chunhua Hu 

Guoqiang Hu 

Hongsheng Hu 

Jwu-Sheng Hu 

Tianjiang Hu 

Zhencheng Hu 

Changchun Hua 

Ana Huaman 

Albert S. Huang

Cheng-Ming Huang 

Chih-Fang Huang 

Haibo Huang 

Han-Pang Huang 

Haomiao Huang 

Jian Huang 

Jian Huang 

Ke Huang 

Qiang Huang 

Ruining Huang 

Shoudong Huang 

Shuguang Huang 

Shuo Huang 

Tzu-Hao Huang 

Yazhou Huang 

Daniel Huber 

Manfred Huber 

Christian Hubicki 

Kai Huebner 

Joel C. Huegel 

Zhao Huihua 

Jan Huissoon 

Thomas Hulin 

James Sean Humbert 

Terry Huntsberger 

Jonathan Hurst 

Ammar Husain 

Islam Hussein 

Manfred Husty 

Seth Hutchinson 

Gilgueng Hwang 

Heeseon Hwang 

Jung-Hoon Hwang 

Kazuyuki Hyodo 

Sang-Ho Hyon 

Baro Hyun 

Heikki Sakari Hyyti 

Karl Iagnemma 

Ion Iancu 

Akihiko Ichikawa 

Romain Iehl 

Siohoi Leng 

Fumiya Iida 

Kojiro Iizuka 

Atsutoshi Ikeda 

Yoshito Ikemata 

Shuhei Ikemoto 

Yusuke Ikemoto 

Viorela Ila 

Boyko Iliev 

Jarmo Ilonen 

Atsushi Imiya 

Gokhan Ince 

Giovanni Indiveri 

Marina Indri 

Francois Felix Ingrand 

Kenji Inoue 

Takahiro Inoue 

Luca Iocchi 

Iulian Iordachita 

Ioannis Iossifidis 

Masatsugu Iribe 

Josep Isern-González 

Genya Ishigami 

Akio Ishiguro 

Hiroyuki Ishii 

Kazuo Ishii 

Jun Ishikawa 

Volkan Isler 

Daigoro Isobe 

Kazuyuki Ito 

Keiichiro Ito 

Tomotaka Itoh 

Iñaki Iturrate 

Serena Ivaldi 

Ioan Alexandru Ivan 

Yoshio Iwai 

Masami Iwase 

Yumi Iwashita 

Hiroyasu Iwata 

Yasushi Iwatani 

Martin Jagersand 

Leonard Jaillet 

Advait Jain 

Dominik Jain 

Ravi Kant Jain 

Michael Jakuba 

Doug L. James 

Rodrigo Jamisola 

Mo Jamshidi 

Farrokh Janabi-Sharifi 

Balazs Janko 

Pakpong Jantapremjit 

Alberto Jardon Huete 

Nathanaël Jarrassé 

Luc Jaulin 

C.V. Jawahar 

Graylin Jay 

Suhada Jayasuriya 

Chandimal Jayawardena 

Jagadeesan Jayender 

Changsoo Je 

Michael Jenkin 

Leif P. Jentoft 

Jeong hwan Jeon 

Jay Jeong 

Mun-Ho Jeong 

Seonghee Jeong 

Sabina Jeschke 

Nikolay Jetchev 

Sang Hoon Ji 

Zhengping Ji 

Yan-Bin Jia 

Zhaoyin Jia 

Guangying Jiang 

Shu Jiang 

Xin Jiang 

Yun Jiang 

Jiyong Jin 

Sungho Jo 

Rolf Johansson 

Aaron Johnson 

Andrew Johnson 

Collin Johnson 

Miles Johnson 

Matthew Johnson-Roberson 

Magnus Johnsson 

Dominik Joho 


–xiii– 

Cyril Joly 

Edward Gil Jones 

Ben Jonker 

Brett Jordan 

Ajay Joshi 

Ming-Shaung Ju 

Zhaojie Ju 

Simon Justin Julier 

Hong-Gul Jun 

Boyoon Jung 

Changbae Jung 

Min Yang Jung 

Yoon C. Jung 

Michael Kaess 

Satoshi Kagami 

Lueder Alexander Kahrs 

Shuuji Kajita 

Mrinal Kalakrishnan 

Vinutha Kallem 

Pasi Johannes Kallio 

Marcelo Kallmann 

Tetsushi Kamegawa 

Mitsuhiro Kamezaki 

Akiya Kamimura 

Hiroshi Kaminaga 

Sören Kammel 

Roman Kamnik 

Stratis Kanarachos 

Takefumi Kanda 

Fumio Kanehiro 

Kenji Kaneko 

Makoto Kaneko 

Yeonsik Kang 

Balajee Kannan 

Dimitrios Kanoulas 

George Kantor 

Imin Kao 

Ankur Kapoor 

Sertac Karaman 

Michael Karg 

Nikhil Karnad 

Amir Karniel 

Joanna Karpinska 

George Karras 

Sisir Karumanchi 

Dusko Katic 

Naomi Kato 

Max Katsev 

Pantelis Katsiaris 

Dov Katz 

Lydia Kavraki 

Hideki Kawahara 

Tatsuya Kawahara 

Atsushi Kawakami 

Kazuya Kawamura 

Sadao Kawamura 

Hiroshi Kawano 

Hiroshi Kawasaki 

Hiroaki Kawashima 

Kenji Kawashima 

Hirohiko Kawata 

Kentaro Kayama 

Richard Kelley 

Jonathan Kelly 

Anssi Juhani Kemppainen 

Farid Kendoul 

Benjamin Kent 

Lychek Keo 

Mehrdad R. Kermani 

Serge Kernbach 

Kristian Kersting 

Samuel B. Kesner 

Saad Khan 

Piyush Khandelwal 

Abderrahmane Kheddar 

Kazuo Kiguchi 

Takehito Kikuchi 

Ryo Kikuuwe 

Byeong-Sang Kim 

Byoung-Ho Kim 

Chang Young Kim 

ChangHwan Kim 

Chanki Kim 

Chyon Hae Kim 

Doik Kim 

Dong Hwan Kim 

Donghyeon Kim 

H. Jin Kim 

Hee-Young Kim 

Hongho Kim 

Jinwook Kim 

Jiwoong Kim 

Jong-Hwan Kim 

Jong-Wook Kim 

Jonghoek Kim 

Jongwon Kim 

Joo H. Kim 

Jung Kim 

Jung-Yup Kim 

Junggon Kim 

Keehoon Kim 

Kyung-Joong Kim 

MinJun Kim 

Sangbae Kim 

Whee Kuk Kim 

Yongtae Kim 

Young J. Kim 

Youngmoo Kim 

Shinichi Kimura 

Chih-Hung King 

H. Hawkeye King 

Hitoshi Kino 

James Kinsey 

Tetsuya Kinugasa 

Zsolt Kira 

Frank Kirchner 

Nathan Kirchner 

Alexis Kirke 

Nobuyuki Kita 

Yasuyo Kita 

Bernd Kitt 

Gregor Klancar 

Ulrich Klank 

Alexander Kleiner 

Andrew Klesh 

Denis Klimentjew 

Stephen Kloder 

Markus Klotzbuecher

Boris Kluge 

Pawel Kmiotek 

Laurent Kneip 

Ross A Knepper 

Heather Knight 

Alois Knoll 

George Knopf 

Jeremie Knuesel 

Kheng Lee Koay 

Etsuko Kobayashi 

Futoshi Kobayashi 

Hiroyuki Kobayashi 

Taizo Kobayashi 

Yo Kobayashi 

Yuichi Kobayashi 

Jens Kober 

Marin Kobilarov 

Sarath Kodagoda 

Kenri Kodaka 

Masanao Koeda 

James Koeneman 

Nathan Koenig 

Koichi Koganezawa 

Kiminao Kogiso 

Norihiro Koizumi 

Nigel Kojimoto 

Ewa Kolakowska 

Maxim Kolesnikov 

Thomas Kollar 

Andreas Kolling 

J. Zico Kolter 

Erik Komendera 

Haldun Komsuoglu 

Taku Komura 

Takashi Komuro 

Kazuyuki Kon 

Toshiyuki Kondo 

Fanyu Kong 

Xianwen Kong 

Rainer Konietschke 

Atsushi Konno 

Kurt Konolige 

Michail Kontitsis 

Ioannis Kontolatis 

Masashi Konyo 

Ig Mo Koo 

Ja Choon Koo 

Gert Kootstra 

Petar Kormushev 

G. Ayorkor Korsah 

David Kortenkamp 

Gabor Kosa 

Hatice Kose-Bagci 

Jana Kosecka 

Yoshihiko Koseki 

D. I. Kosmopoulos 

Kazuhiro Kosuge 

Shinya Kotosaka 

Navinda Kottege 

Mirko Kovac 

Gerhard Kraetzschmar 

Dirk Kraft 

Florian Kraft 

Danica Kragic 

Michael Krainin 

Bradley Kratochvil 

Frédéric Kratz 

Hermano Igo Krebs 

Philipp Kremer 

Ingo Kresse 

Henrik Kretzschmar 

Simon Kriegel 

Madhava Krishna 

Torsten Kroeger 

Oliver Kroemer 

Athanasios Krontiris 

Kristian Kroschel 

Volker Krueger 

Alexandre Krupa 

Sebastien Krut 

Maarja Kruusmaa 

Jiun-Yih Kuan 

Wilfried Kubinger 

Takashi Kubota 

Daniel Kubus 

Shunsuke Kudoh 

Bernhard Kuebler 

Rainer Kuemmerle 

James Kuffner 

Benjamin Kuipers 

Dana Kulic 

Ganesh Kumar 

Manish Kumar 

Prasanth Kumar 

Rajesh Kumar 

Vijay Kumar 

Yasuharu Kunii 

Yoshinori Kuno 

Tobias Kunz 

I Han Kuo 

Victor Kuo 

Kentarou Kurashige 

Ryo Kurazume 

Masamitsu Kurisu 

Yuichi Kurita 

Yoji Kuroda 

Haruhisa Kurokawa 

Yoshiaki Kuwata 

Dong-Soo Kwon 

Hyukseong Kwon 

Hyunki Kwon 

Ohung Kwon 

SangJoo Kwon 

Yong Moo Kwon 

Young-Sik Kwon 

Kolja Kühnlenz 

Ville Kyrki 

Erik Kyrkjebø 

Bruno L'Esperance 

Hung La 

Janne Laaksonen 

Ouiddad Labbani-Igbida 

Francisco Lacerda 

Bakir Lacevic 

Simon Lacroix 

Matteo Laffranchi 

Chin-Lun Lai 

Kevin Lai 


–xiv– 

Yongjun Lai 

Sara Lal 

Thierry Laliberte 

Jean-Francois Lalonde 

Chi Pang Lam 

Raymond Lam 

Olivier Lambercy 

Florent Lamiraux 

Pierre Lamon 

Roberto Lampariello 

Christoph H. Lampert 

Chao-Chieh Lan 

David Lane 

Christian Lang 

Jochen Lang 

Tobias Lang 

Friedrich Lange 

Sascha Lange 

Jacob Langelaan 

Jørgen Larsen 

Amy Larson 

Cecilia Laschi 

Boris Lau 

Darwin Lau 

Manfred Lau 

Nuno Lau 

Tak Kit Lau 

Tim Laue 

Martin Lauer 

Christian Laugier 

Steven M. LaValle 

Brian Lawson 

Maria Teresa Lazaro 

Olivier Le Meur 

Jerome Le Ny 

Brigitte Le-Pévédic 

Chil-Woo Lee 

Daewon Lee 

Daniel D. Lee 

Dongheui Lee 

Doo Yong Lee 

Hyunglae Lee 

Hyunsuk Lee 

Jae Hoon Lee 

Jaewoo Lee 

Ji-Yeong Lee 

Jihong Lee 

Jonghyun Lee 

Ju-Jang Lee 

Juhyun Lee 

Kiju Lee 

Kyoung Mu Lee 

Loo Hay Lee 

Mark Lee 

Sanghoon Lee 

Sangyoon Lee 

Se-Jin Lee 

Seongsoo Lee 

Seung-Mok Lee 

Sukhan Lee 

Wen-Yo Lee 

Woosub Lee 

Yun-Jung Lee 

Benjamin Lefaudeux 

Dirk Lefeber 

Stéphanie Lefèvre 

Jie Lei 

Antonio C. Leite 

Sylvie Lelandais 

Roland Lenain 

Sebastien Lengagne 

Scott Lenser 

Tommaso Lenzi 

John Leonard 

Simon Leonard 

Matteo Leonetti 

Vincent Lepetit 

Ilkka M. Leppänen 

Frederic Lerasle 

Charles Lesire 

Adrian Leu 

Keith Yu Kit Leung 

Christopher Lewis 

Jeremy Lewis 

M. Anthony Lewis 

Michael Lewis 

Christian Lexcellent 

Sigrid Leyendecker 

Bin Li 

Changchun Li 

Cheng Gang Li 

Guangyong Li 

Haizhou Li 

Hui Li 

Jingliang Li 

Min Li 

Ming Li 

Peng Li 

Qinchuan Li 

Ruonan Li 

Shigang Li 

Tsai-Yen Li 

Xiang Li 

Xingyan Li 

Yangming Li 

Yi Li 

Yuan Yuan Li 

Yuanping Li 

Yuwen Li 

Zhibin Li 

Zhibin Li 

Zhijun Li 

Qiaokang Liang 

Xinwu Liang 

Yao Liang 

Hongen Liao 

Minas Liarokapis 

Somchaya Liemhetcharat 

Jyh-Ming Lien 

Neal Y Lii 

Pål Liljebäck 

Chee Wang Lim 

Jongwoo Lim 

Kyeong Bin Lim 

Sejoon Lim 

Ser-Nam Lim 

Wenbin Lim 

Bor-Shen Lin

Chia-How Lin 

Kuen-Han Lin 

Lanny Lin 

Pei-Chun Lin 

Shih Chi Lin 

Wei Lin 

Yu Lin 

Yucong Lin 

Zhiyun Lin 

Zhuohua Lin 

Magnus Linderoth 

Laura Lindzey 

Qiang Ling 

Kai Lingemann 

Vittorio Lippi 

Vincenzo Lippiello 

Michael Lipsett 

Bingbing Liu 

Chao Liu 

Chenggang Liu 

Fangfang Liu 

Guanfeng Liu 

Guangjun Liu 

Hanwei Liu 

Hong Liu 

Hong Liu 

Hongbin Liu 

Honghai Liu 

Jie Liu 

Jindong Liu 

Jing Liu 

Jing-Sin Liu 

Jinguo Liu 

Karen Liu 

Lianqing Liu 

Minjie Liu 

Rongqiang Liu 

Tao Liu 

Tong Liu 

Xinyu Liu 

Yang Liu 

Yang Liu 

Yong Liu 

Yugang Liu 

Fernando Lizarralde 

Xavier Lladó 

Jorge Lobo 

Vo-Gia Loc 

Dario Lodi Rizzini 

Sauro Longhi 

Rosemarijn Looije 

Manuel Lopes 

Natalia Lopez Celani 

Carlos Al. López-Franco 

Ismael Lopez-Juarez 

Rigoberto Lopez-Padilla 

Antonio Loria 

Yunjiang Lou 

Miguel Lourenço 

Bryan Kian Hsiang Low 

K. H. Low 

Thomas Low 

Robert Lowe 

Rogelio Lozano 

Tomas Lozano-Perez 

Qi Lu 

Tien-Fu Lu 

Yan Lu 

Yanyan Lu 

Yi Lu 

Tomasz Lubecki 

Matthias Luber 

Carlos Luck 

Brandon Luders 

Greg R. Luecke 

Ching-Hu Luh 

Wen Lik Dennis Lui 

Henk Luinge 

Christopher Lum 

Ryan Luna 

Jun Luo 

Ren Luo 

Zhiqiang Luo 

Sergei Lupashin 

Alexis Lussier Desbiens 

Goran Lynch 

Kevin Lynch 

Daniel Lyons 

Ingo Lütkebohle 

Martin Lösch 

Hongbo Ma 

Jeremy Ma 

Shugen Ma 

Heiko Maass 

Bruce MacDonald 

D. Guimarães Macharet 

Doug MacKenzie 

Robert A. MacLachlan 

David MacNair 

Yasushi Mae 

Guilherme Jorge Maeda 

Shingo Maeda 

Andreas Maeder 

Shoichi Maeyama 

Gianantonio Magnani 

Martin Magnusson 

Aditya Mahadevan 

Ian Mahon 

Robert Mahony 

Mohammad Mahoor 

Mohsen Mahvash 

Daniel Maier 

Jim Mainprice 

Perla Maiolino 

Elmar Mair 

Jeremy Maitin-Shepard 

Andras Majdik 

Carmel Majidi 

Toshihiro Maki 

Masaaki Makikawa 

Satoshi Makita 

Michail Makrodimitris 

Alexis Maldonado 

Anthony Mallet 

Angelos Mallios 

Kenneth Mallory 

Abed Malti 

Noureddine Manamanni 


–xv– 

Kasra Manavi 

Ian Manchester 

Roberto Manduchi 

Olivier Mangin 

Kalyan Mankala 

Justin Mann 

Nicolas Mansard 

Zhi-Hong Mao 

Panos Marantos 

James Marble 

Eric Marchand 

Andrew Marchese 

Luca Marchetti 

Lorenzo Marconi 

Rebeca Marfil 

Luis Felipe Marin-Urias 

Dimitri Marinakis 

Alessandro Marino 

Gian Luca Mariottini 

Ali Marjovi 

Ivan Markovic 

Lino Marques 

Steven Marra 

Jose Luis Marroquin 

Joshua A. Marshall 

Sylvain Martel 

Anne Martin 

Patrick Martin 

Steven Colin Martin 

Ester Martinez 

Oscar Martinez Mozos 

Harold R. Martinez Salazar 

Ruben Martinez-Cantin 

J.R. Martínez-de Dios 

Simone Martini 

Alcherio Martinoli 

Eric Martinson 

Zoltan-Csaba Marton 

Noriaki Maru 

Hisataka Maruyama 

Ignacio Mas 

Ken Masamune 

Lorenzo Masia 

Julian Mason 

Ahmad A. Masoud 

Hiroyuki Masuta 

Maja Mataric 

Bogdan Matei 

T, William Mather 

George Mathews 

Fernando Matia 

Takamitsu Matsubara 

Osamu Matsumoto 

Yoshio Matsumoto 

Takayuki Matsuno 

Daisuke Matsuura 

Kenichiro Matsuzaki 

Dennis Matthews 

Larry Matthies 

Giuliana Mattiazzo 

Rosana Matuk Herrera 

Saburo Matunaga 

Christophe Maufroy 

Francesco Maurelli 

Iñaki Maurtua 

S. Mohammad Mavadati 

Constantinos Mavroidis 

Bruce Maxwell 

Stephen Maybank 

Jerome Maye 

Christoph Mayer 

Walterio Mayol 

Anirban Mazumdar 

Barbara Mazzarino 

Barbara Mazzolai 

Stefano Mazzoleni 

Lachlan McCalman 

Chris McCarthy 

Samuel McCarthy 

Erik McDermott 

Christopher McFarland 

Martin McGinnity 

Michael McHenry 

Gerard McKee 

David McKinnon 

T.W. McLain 

James McLurkin 

John J. McPhee 

Timothy McPherson 

Rafik Mebarki 

Lashika Medagoda 

Gustavo Medrano-Cerda 

Debbie Meduna 

Sanford Meek 

Wim Meeussen 

David Paul Meger 

Malika Meghjani 

Syed Bilal Mehdi 

Mehran Mehrandezh 

Siddhartha Mehta 

Christopher Mei 

Luis Mejias 

Claudio Melchiorri 

Daniel Mellinger 

Rochelle Mellish 

Arianna Menciassi 

Caio Mendes 

Ricardo F. Mendoza Garcia 

Emanuele Menegatti 

Felipe Meneguzzi 

Paulo Menezes 

Yan Meng 

Tekin Mericli 

Luis Merino 

Jean-Pierre Merlet 

Gregory Mermoud 

Torsten Merz 

Elena Messina 

Uwe Mettin 

Daniel Meyer-Delius 

Youcef Mezouar 

Krzysztof Mianowski 

Alain Micaelli 

Nathan Michael 

Andreas Michaels 

Francois Michaud 

Fabien Michel 

Bernard Michini

Davide Migliore 

Matjaž Mihelj 

David Mikesell 

Michael J Milford 

Isaac Miller 

James K. Mills 

Bojan Milosevic 

Adam Milstein 

Dejan Milutinovic 

Hyeun Jeong Min 

Mamoru Minami 

Aiguo Ming 

Nicoleta Minoiu Enache 

Mark Minor 

Sylvain Miossec 

Luiz Gustavo Mirisola 

Faraz Mirzaei 

Marcell Missura 

Michael Mistry 

Seiichi Mita 

Atsushi Mitani 

Kazuhisa Mitobe 

Nikos Mitsou 

Mamoru Mitsuishi 

Noriaki Mitsunaga 

Jun Miura 

Kanako Miura 

Toyomi Miyagawa 

Seiichi Miyakoshi 

Shuhei Miyashita 

Takahiro Miyashita 

Mehrdad Moallem 

Hiromi Mochiyama 

Joseph Modayil 

Birgit Moeller 

Richard Phillip Mohamed 

Mahmood Mohammadi 

Samer Mohammed 

Vishwanathan Mohan 

Kamran Mohseni 

Shahram Mohseni-vahed 

Rezia Molfino 

Mark Moll 

Vito Monaco 

Francesco Mondada 

Raúl Monroy 

Luis Montano 

Luis Montesano 

Eduardo Montijano 

Chang-bae Moon 

Hyungpil Moon 

Jae-Sung Moon 

Brian Moore 

Joseph Moore 

Richard J.D. Moore 

Frank Moosmann 

Andres Mora 

Hadi Moradi 

Antonio Morales 

Eduardo Morales 

Jesús Morales 

Marco Morales 

Luis Yoichi Morales Saiki 

Fabio Morbidi 

Guillaume Morel 

Yannick Morel 

Vassilios Morellas 

Jan Morén 

Juan Camilo Moreno 

Luis Moreno 

Francesc Moreno-Noguer 

Kristi Morgansen 

Takeshi Mori 

Taketoshi Mori 

Yasushi Morikawa 

Mitsuharu Morisawa 

Takuro Moriyama 

Nicholas Morizio 

Nicholas Morozovsky 

Daniel D. Morris 

Peter Morton 

Ryan Morton 

Mark Moseley 

Alejandro R. Mosteo 

El Mustapha Mouaddib 

Tetsuya Mouri 

Anastasios Mourikis 

Annan Mozeika 

Andreas Mueller 

Joerg Mueller 

Katharina Muelling 

Jonathan Mugan 

Ranjan Mukherjee 

Rudranarayan Mukherjee 

Albert Mukovskiy 

Enzo Mumolo 

Marko Munih 

Luis Miguel Munoz 

Victor Muñoz 

Riccardo Muradore 

Kenichi Murakami 

Kouji Murakami 

Toshiyuki Murakami 

Ana Cristina Murillo 

Todd Murphey 

Chris Murphy 

David Murray 

Richard Murray 

Rafael Murrieta-Cid 

Giovanni Muscato 

Shabbir K. Mustafa 

Bilge Mutlu 

George Mylonas 

Holger Mönnich 

Thomas Mörwald 

S. Nadubettu Yadukumar 

Hajime Nagahara 

Kiyoshi Nagai 

Takayuki Nagai 

Yukie Nagai 

Hiroyuki Nagamatsu 

Kenji Nagaoka 

Umashankar Nagarajan 

Kazuyuki Nagata 

Keiji Nagatani 

Radhika Nagpal 

Prathap Nair 

Michael D. Naish 


–xvi– 

Eiichi Naito 

Zoran Najdovski 

Homayoun Najjaran 

Kazuhiro Nakadai 

Ryu Nakadate 

Masahiro Nakajima 

Akio Nakamura 

Ryoichi Nakamura 

Taro Nakamura 

Yutaka Nakamura 

Hiroaki Nakanishi 

Hiroki Nakanishi 

Jun Nakanishi 

Yuto Nakanishi 

Akira Nakashima 

Atsushi Nakazawa 

Alireza Nakhaei 

Roberto Naldi 

Akio Namiki 

Mehrzad Namvar 

Thrishantha Nanayakkara 

Jun Nango 

Kostas Nanos 

Ashley Napier 

Nils Napp 

Kenichi Narioka 

Oleg Naroditsky 

David Naso 

Sameh Nassar 

Ciro Natale 

Lorenzo Natale 

Shuvra Nath 

Robert Nawrocki 

Shahriar Negahdaripour 

José Neira 

Goldie Nejat 

Bradley J. Nelson 

Carl Nelson 

Bojan Nemec 

Dragomir Nenchev 

Esha Nerurkar 

Issa Nesnas 

Stephen Nestinger 

Eric Nettleton 

Jeremiah Neubert 

Peter Neuhaus 

Bradford Neuman 

Jan Neumann 

Teck Chew Ng 

Victor Ng-Thow-Hing 

Trung Dung Ngo 

Hai Nguyen 

Duy Nguyen-Tuong 

Kai Ni 

Marta Niccolini 

Monica Nicolescu 

Gunter Niemeyer 

Juan Nieto 

Carlos Nieto-Granda 

Misato Nihei 

Ryuma Niiyama 

Janosch Nikolic 

Nattee Niparnan 

Shun Nishide 

Yasutaka Nishioka 

Koichi Nishiwaki 

Ilana Nisky 

Verena Nitsch 

Tomoyuki Noda 

David Noelle 

Hiroshi Noguchi 

Masahiro Nohmi 

Scott Nokleby 

Narges Noori 

Toshiro Noritsugu 

Navid Nourani Vatani 

Illah Reza Nourbakhsh 

Walter Nowak 

Cameron Nowzari 

Shahin Nudehi 

Jesus Nuevo 

Urbano Nunes 

Pedro Núñez Trujillo 

Emmanuel Nuño 

Stephen Nuske 

Rehan O'Grady 

Keith O'Hara 

Jason O'Kane 

Damien O'Rourke 

Oliver Obst 

Tobias Obst 

Manuel Ocaña 

Mitsushige Oda 

Lael Odhner 

Jean-Marc Odobez 

Denny Oetomo 

Tsukasa Ogasawara 

Tetsuya Ogata 

Tetsuji Ogawa 

Koichi Ogawara 

Dimitri Ognibene 

Petter Ogren 

Paul Y. Oh 

Se-Young Oh 

Sehoon Oh 

Songhwai Oh 

Kenichi Ohara 

Kazunori Ohno 

Apollon Oikonomopoulos 

Lauro Ojeda 

Kei Okada 

Masafumi Okada 

Jun Okamoto 

Jun Okamoto Junior 

Allison M. Okamura 

Hiroshi G. Okuno 

Andrej Olenšek 

Nejat Olgac 

Miguel A. Olivares-Mendez 

Paulo Oliveira 

Tiago Roux Oliveira 

Gabriel A. Oliver 

Anibal Ollero 

Daniel Olmeda 

Bjorn Olofsson 

Clark Olson 

Edwin Olson 

Tomas Olsson

Toru Omata 

Cagdas Denizel Onal 

Ahmet Onat 

Hiromu Onda 

Masahiro Ono 

Yizhar Or 

David Orin 

Agustin A. Ortega Jimenez 

Alberto Ortiz 

Daniel Ortiz Morales 

Tobias Ortmaier 

Fernando Osório 

James Ostrowski 

Koichi Osuka 

Jun Ota 

Masatsugu Otsuki 

Christian Ott 

Lionel Ott 

Michael W. Otte 

Mark Ottensmeyer 

Zhicai Ou 

Puren Ouyang 

Dai Owaki 

Jason Owens 

Eimei Oyama 

John O. Oyekan 

Ryuta Ozawa 

Onur Ozcan 

Ali Gürcan Özkil 

Erhan Oztop 

Taskin Padir 

Vincent Padois 

Ali Paikan 

Nicholas Paine 

Ana Lucia Pais 

Jordi Palacin 

Gianluca Palli 

Rainer Palm 

Luther R. Palmer III 

Matteo Claudio Palpacelli 

Jia Pan 

Qinxue Pan 

Ya-Jun Pan 

Dimitra Panagou 

Amit Kumar Pandey 

Shuo Pang 

Dejan Pangercic 

Giorgio Panin 

Athanasia Panousopoulou 

Julien Pansiot 

Caroline Pantofaru 

Evangelos Papadopoulos 

Georgios Papadopoulos 

Nikos Papanikolopoulos 

George J. Pappas 

Byungjae Park 

Edward J. Park 

Ga-Ram Park 

Hyongju Park 

Hyungmin Park 

Jae Byung Park 

Jaeheung Park 

Jong Jin Park 

Sangdeok Park 

Shinsuk Park 

Soonyong Park 

Sukho Park 

Sung-Kee Park 

Wooram Park 

Yong-Jai Park 

Youngjin Park 

Aaron Parness 

Vicente Parra Vega 

Martin Peter Parsley 

Federica Pascucci 

Anatol Pashkevich 

Carolina Passenberg 

Peter Pastor 

Maria Pateraki 

Kaustubh Pathak 

Volkan Patoglu 

Alexandru Patriciu 

Ugo Pattacini 

Alexander Patterson IV 

Rohan Paul 

Frederick Pauling 

Liam Paull 

Dietrich Paulus 

Marco Pavone 

Jonathan Paxman 

Shahram Payandeh 

Lina María Paz 

Roger Pearce 

Federico Pecora 

Luis Pedraza 

Angelika Peer 

Claude Pégard 

Yuanteng Pei 

Paulo Peixoto 

Pietro Peliti 

Ron Pelrine 

Virginia Pensabene 

Ivan Penskiy 

Romain Pepy 

Guilherme Pereira 

Jose Nuno Pereira 

Douglas Perrin 

Nicolas Yves Perrin 

Francesco Pescarmona 

Gustavo Pessin 

Steven Peters 

Henrik Gordon Petersen 

Klaus Petersen 

Cammy Peterson 

Kevin Peterson 

Lars Petersson 

Antoine Petit 

Florian Petit 

Clément Pêtrès 

Tadej Petric 

Ron Petrick 

Anna Petrovskaya 

Kathrin Eva Peyer 

Zachary Pezzementi 

Patrick Pfaff 

Friedrich Pfeiffer 

Frank Pfenning 

Max Pfingsthorn 


–xvii– 

Martin Pfurner 

Minh Tu Pham 

Louis Phee 

Sylvie Philipp-Foliguet 

Roland Philippsen 

Johan Philips 

Mike Phillips 

Thanathorn Phoka 

Emmanuel Piat 

Stefano Piazza 

Brennand Pierce 

Vinay Pilania 

Luciano Pimenta 

Benny Ping Lai Lo 

Pedro Pinies 

Charles Pinto 

Peam Pipattanasomporn 

Tony Pipe 

Charles Pippin 

Gabriel Pires 

Hamed Pirsiavash 

Antonio Pistillo 

Benjamin Pitzer 

Mihail Pivtoraiko 

Oscar Pizarro 

Christian Plagemann 

Erion Plaku 

Robert Platt 

Patrick Plonski 

Frederic Plumet 

Paul G. Plöger 

Kishore Pochiraju 

Janez Podobnik 

Sameera Poduri 

Philippe Poignet 

Dragoljub Pokrajac 

Nancy S Pollard 

Lorenzo Pollini 

Ilia G. Polushin 

Jorge Pomares 

Hasan Poonawala 

Dan Popa 

Josep M Porta 

Ingmar Posner 

Dave Post 

Alexis Potelle 

Veljko Potkonjak 

Andreas Pott 

Ioannis Poulakakis 

Paul Pounds 

Soha Pouya 

Nathan Powel 

Cedric Pradalier 

José Augusto Prado 

David Prasser 

Dilip Kumar Pratihar 

Mario Prats 

Jerry Pratt 

Domenico Prattichizzo 

Samuel Prentice 

Muriel Pressigout 

Edson Prestes 

Alberto Pretto 

Libor Preucil 

Carsten Preusche 

Andrzej Pronobis 

Nicolas Pronost 

Amanda Prorok 

Markus Przybylski 

Gustavo Armando Puerto 

Luis Puig 

Luca Pulina 

Bing Qiao 

Hong Qiao 

Tian Qiu 

Xingda Qu 

Giuseppe Quaglia 

Morgan Quigley 

Roger, D. Quinn 

Annika Raatz 

Amina Radgui 

Katayon Radkhah 

Amir Rahmani 

Talal Rahwan 

Jurdak Raja 

Micky Rakotondrabe 

Srikumar Ramalingam 

Subramanian Ramamoorthy 

Amar Ramdane-Cherif 

Nacim Ramdani 

Arnau Ramisa 

Nadeesha Ranasinghe 

Ananth Ranganathan 

Pradeep Ranganathan 

Markus Rank 

Shrisha Rao 

Mohammad Rastgaar 

Ravi Kulan Rathnam 

Nathan Ratliff 

Banahalli Ratna 

Georg Rauter 

Barak Raveh 

Prabu Ravindran 

Konrad Rawlik 

Anjan Kumar Ray 

Francesco Rea 

Pierluigi Rea 

Christopher M. Reardon 

Brice Rebsamen 

Gabriel Recatala 

Signe Redfield 

Brooks Reed 

Kyle Brandon Reed 

Monica Reggiani 

Stéphane Régnier 

Rob Reilink 

Oscar Reinoso 

Luís Paulo Reis 

Ulrich Reiser 

Dustin Reishus 

Georgios Rekleitis 

C. David Remy 

Ping Ren 

Wei Ren 

Xiaofeng Ren 

Pierre Renaud 

Maria Joao Rendas 

Mark Rentschler

Filoktimon Repoulias 

Ari Requicha 

David Ribas 

Isabel Ribeiro 

Rogerio Richa 

Arthur Richards 

Andrew Richardson 

Markus Rickert 

Jose-de-Jesus Rico 

Robert Riener 

Hala Rifai 

Ludovic Righetti 

Gerhard Rigoll 

Marcia Riley 

Helge Joachim Ritter 

Alejandro Rituerto 

Cameron Riviere 

Maximo A. Roa 

Richard Roberts 

Rodney Roberts 

Anders Robertsson 

Paolo Rocco 

Rui Paulo Rocha 

Eduardo Rocon 

Tobias Rodemann 

Adrian Rodriguez 

Alberto Rodriguez 

Carlos Rodriguez 

F. de Borja Rodríguez 

Marcela Patricia Rodriguez 

Samuel Rodriguez 

A.Rodríguez Tsouroukdissian 

F. Rodriguez y Baena 

Erick J. Rodriguez-Seda 

Arne Roennau 

John G. Rogers III 

Se-gon Roh 

John Roland 

Chris Roman 

Joseph M. Romano 

Eric Rombokas 

Lotfi Romdhane 

Antonio Romeo 

Javier Romero 

R. Ap. Francelin Romero 

M.-A. Romero-Ramirez 

Lluis Ros 

Carlos Rosales 

Jan Rosell 

Jacob Rosen 

Stephanie Rosenthal 

Robert Ross 

Claudio Rossi 

Juergen Rossmann 

Gerhard Roth 

Axel Rottmann 

Pierre Rouanet 

Stergios Roumeliotis 

Giannis Roussos 

Anthony Rowe 

Nicholas Roy 

Eric Royer 

Michael Rubenstein 

Martin Rufli 

Fabio Ruggiero 

Michael Ruhnke 

Andy Ruina 

Fabio Ruini 

Federico Ruiz 

Ubaldo Ruiz 

Javier Ruiz-del-Solar 

Wheeler Ruml 

Juergen Rummel 

Ian Rust 

Malcolm Ryan 

Paul E. Rybski 

Julian Ryde 

Jee-Hwan Ryu 

Jeha Ryu 

Thomas Röfer 

Thorsteinn Rögnvaldsson 

Selma Sabanovic 

Jose M. Sabater Navarro 

Lorenzo Sabattini 

Christophe Sabourin 

Parvaneh Saeedi 

Ryo Saegusa 

Alessandro Saffiotti 

Satoshi Saga 

Jody Alessandro Saglia 

Ranjana Sahai 

Erol Sahin 

Satoru Sakai 

Paolo Salaris 

Camille Salaun 

Septimiu E. Salcudean 

Antonino Salerno 

Curt Salisbury 

Roque Saltaren 

Giampiero Salvi 

Joaquim Salvi 

Gionata Salvietti 

Paul Samuel 

Siddharth Sanan 

Emilio Sánchez 

Abraham Sánchez López 

Gildardo Sanchez-Ante 

William Sandberg 

Arthur Sanderson 

Giulio Sandini 

Corina Sandu 

Alberto Sanfeliu 

Hyon Sang-Ho 

Kiattisak Sangpradit 

Ganesh Sankaranarayanan 

Pedro Santana 

Cristina Santos 

Veronica J. Santos 

Vitor Santos 

Amin Sarafraz 

Sezimaria F. P. Saramago 

Afsar Saranli 

Uluc Saranli 

Shigeru Sarata 

Irene Sardellitti 

Sanem Sariel-Talay 

Nilanjan Sarkar 

Hiroshi Saruwatari 


–xviii– 

Yoko Sasaki 

Martin Saska 

Shivakumar Sastry 

RM Satava 

M. Sathia Narayanan 

Aykut Cihan Satici 

Massimo Satler 

Daisuke Sato 

Noritaka Sato 

Junaed Sattar 

Joe Saunders 

Guillaume Saupin 

Eric Sauser 

Jean-Philippe Saut 

Jesus Savage 

Ketan Savla 

Glauco Garcia Scandaroli 

Davide Scaramuzza 

Umberto Scarfogliero 

Paul Scerri 

Stefan Schaal 

Thomas Schauß 

Stefano Scheggi 

Michael Scheint 

Sebastian Scherer 

Paul Schermerhorn 

Alexis Scheuer 

Giuseppina Schiavone 

Andre Schiele 

Andreas Schierl 

Julian Schill 

Lars Schillingmann 

Joseph Schimmels 

Alexander Schlaefer 

Christian Schlegel 

C. Schmidt-Wetekam 

Alexander Schmitz 

Axel Schneider 

Sven Schneider 

Christian Schnier 

Jonathan Scholz 

Debra Schreckenghost 

Hans Peter Schrocker 

Derik Schroeter 

Björn Schuller 

Niclas Schult 

Ulrik Pagh Schultz 

Dirk Schulz 

Hannes Schulz 

Mac Schwager 

Maxim Schwartz 

Bernd Schäfer 

Enea Scioni 

Stephen Se 

Luís Seabra Lopes 

Jose Maria Sebastian 

Cristian Secchi 

Vlad Seghete 

Stephan Sehestedt 

Masahiro Sekimoto 

Claudio Semini 

Kei Senda 

Taku Senoo 

Jonathon Sensinger 

Luis Sentis 

TaeWon Seo 

Young-Woo Seo 

Joao Sequeira 

Fabrizio Sergi 

Andrea Serrani 

Fabien Servant 

Travis Service 

Andre Seyfarth 

Antonio Sgorbissa 

Robert Shade 

Azad Shademan 

Danelle Shah 

Shridhar Shah 

Hossein Shahbazi 

Azamat Shakhimardanov 

Elie Shammas 

Zeyong Shan 

Weiwei Shang 

Xiaowei Shao 

Zhufeng Shao 

Amir Shapiro 

Inna Sharf 

Rajnikant Sharma 

Dvijesh Shastri 

Cheng Yap Shee 

Raymond Ka-Man Sheh 

Amarda Shehu 

Dylan Shell 

Jinglin Shen 

Shuhan Shen 

Wei-Min Shen 

Xiangrong Shen 

Yanjun Shen 

Yantao Shen 

Apoorva Shende 

Weihua Sheng 

Jinbo Shi 

Qing Shi 

Mizuho Shibata 

Takanori Shibata 

Tomohiro Shibata 

Zvi Shiller 

David Hyunchul Shim 

Masahiro Shimizu 

Masayuki Shimizu 

Toshimi Shimizu 

Shingo Shimoda 

Makoto Shimojo 

Masamichi Shimosaka 

Dongjun Shin 

YongDeuk Shin 

Hiroyuki Shinoda 

Patrick Yuri Shinzato 

Masahiro Shiomi 

Shun Shiramatsu 

Bijan Shirinzadeh 

Babak Shirmohammadi 

Ming-Chiuan Shiu 

Alexander Shkolnik 

Florian Shkurti 

Steven Shladover 

Shraga Shoval 

Gabe Sibley

Bruno Siciliano 

Candace Sidner 

Daniel Sidobre 

Roland Siegwart 

Basilio Sierra 

Olivier Sigaud 

Ricardo Silva Souza 

Geraldo Silveira 

David Silver 

Carlos Silvestre 

Rob Sim 

Nabil Simaan 

Olivier Simonin 

Richard Simpson 

Jivko Sinapov 

Amarjeet Singh 

Sanjiv Singh 

Surya Singh 

Edoardo Sinibaldi 

Ryan W. Sinnet 

Hebertt Sira-Ramírez 

Daniel Sirkett 

Shahin Sirouspour 

Emrah Akin Sisbot 

Metin Sitti 

Dimitrios Skarlatos 

Henrik Skibbe 

Danijel Skocaj 

Alexander Skoglund 

Goran Skorja 

William Smart 

Claes Christian Smith 

James Smith 

Joshua R. Smith 

Ryan Smith 

Stephen L. Smith 

Ruben Smits 

Jamie Snape 

Paolo Soda 

Joan Solà 

Massimiliano Solazzi 

Jorge Solis 

Hyoung Il Son 

Dan Song 

Dezhen Song 

Guangming Song 

Kai-Tai Song 

Qi Song 

Xuan Song 

Zhangjun Song 

Zhibin Song 

Olof Sornmo 

Domenico G. Sorrenti 

Edoardo Sotgiu 

Francois Soumis 

Cristóvão Sousa 

Giacomo Spampinato 

Diana F. Spears 

Stefanie Speidel 

Matthew Spenko 

Fabien Spindler 

Luciano Spinello 

John Spletzer 

Mark Spong 

Alexander Sproewitz 

Christoph Sprunk 

Mohan Sridharan 

Siddhartha Srinivasa 

Manoj Srinivasan 

Cyrill Stachniss 

Maciej Stachura 

Sergiu-Dan Stan 

Bogdan Stanciulescu 

Milos Stankovic 

Aaron Staranowicz 

Olivier Stasse 

Christoph Staub 

Bastian Steder 

Mark Steedman 

Paolo Stegagno 

Jochen J. Steil 

Gerald Steinbauer 

Aaron Steinfeld 

Erik Steltz 

Annett Stelzer 

Björn Stenger 

Benjamin Stephens 

Yiannis Stergiopoulos 

Bruno Steux 

Nicholas Stiffler 

Christoph Stiller 

Daniel Stilwell 

Serge Stinckwich 

Timothy Stirling 

Leo Stocco 

Dan Stoianovici 

Rustam Stolkin 

Andreas Stolt 

Kasper Stoy 

Danail Stoyanov 

Todor Stoyanov 

Alexander Stoytchev 

Stefano Stramigioli 

Morten Strandberg 

Hauke Strasdat 

Olivier Strauss 

Johannes H. Strom 

Freek Stulp 

Jürgen Sturm 

Peter Sturm 

Nathan Sturtevant 

Jörg Stückler 

Michael Styer 

Hao Su 

Francisco Suarez 

Raul Suarez 

Halit Bener Suay 

Ram Subramanian 

Ioan Alexandru Sucan 

Jozef Suchý 

Attawith Sudsang 

Hiromichi Suetani 

Yusuke Sugahara 

Taisuke Sugaiwa 

Thomas Sugar 

Tomomichi Sugihara 

Norikazu Sugimoto 

Yasuhiro Sugimoto 


–xix– 

Komei Sugiura 

Yuta Sugiura 

Masashi Sugiyama 

Osamu Sugiyama 

P.B. Sujit 

Gaurav Sukhatme 

Wael Suleiman 

Khan Suleman 

Cornel Sultan 

Yasushi Sumi 

Dong Sun 

Hanxu Sun 

Kuan-Chun Sun 

Kui Sun 

Qiao Sun 

Xiaoxun Sun 

Zhenglong Sun 

Joseph Sun de la Cruz 

YoonChang Sung 

Michael Suppa 

Kenji Suzuki 

Takashi Suzuki 

Tatsuya Suzuki 

Tsuyoshi Suzuki 

Mikhail Svinin 

Lee Sword 

Filip Szufnarowski 

Klementyna Szwaykowska 

Duc Anh Ta 

Seyed Nasr Tabatabaei 

Armando Tacchella 

Kenjiro Tadakuma 

Riichiro Tadakuma 

Satoshi Tadokoro 

Fabrizio Taffoni 

Hamid Taghirad 

Kenji Tahara 

Adnan Tahirovic 

Dave Tahmoush 

Omar Tahri 

Michel Taïx 

Kouichi Taji 

Keiki Takadama 

Motoki Takagi 

Junji Takahashi 

Masaki Takahashi 

Toru Takahashi 

Takeshi Takaki 

Atsuo Takanishi 

Wataru Takano 

Hidenori Takauji 

Leila Takayama 

Toshinobu Takei 

Hiroshi Takemura 

Yasunori Takemura 

Hiroki Takeuchi 

Hironori Takimoto 

Tomohito Takubo 

Takashi Takuma 

Mehdi Tale Masouleh 

Ali Talebi 

Levente Tamas 

Minija Tamosiunaite 

U-Xuan Tan 

Xiaobo Tan 

Youhua Tan 

Kanji Tanaka 

Masayuki Tanaka 

Yoshihiro Tanaka 

Yoshiyuki Tanaka 

Chaoquan Tang 

Chinpei Tang 

Hsiao-Wei Tang 

Xinyu Tang 

James Tangorra 

Kazuhiro Taniguchi 

Tadahiro Taniguchi 

Yong Tao 

Lydia Tapia 

Adriana Tapus 

Jean-Philippe Tarel 

Mihai Olimpiu Tatar 

Fujiura Tateshi 

Mahmoud Tavakoli 

Abdelhamid Tayebi 

Camillo Jose Taylor 

Matthew Taylor 

Yuichi Tazaki 

Krzysztof Tchon 

Russ Tedrake 

Bruno O.S. Teixeira 

Onur Tekdas 

Seth Teller 

Thomas Temple 

Ernesto H. Teniente Avilés 

Moritz Tenorth 

Kenji Terabayashi 

Carlos R. Tercero Villagran 

Alexander V. Terekhov 

Marco Henrique Terra 

Zdravko Terze 

Matthew Tesch 

Luka Teslic 

Celine Teuliere 

Evangelos Theodorou 

Dirk Thomas 

Federico Thomas 

Stephen James Thomas 

Andrea Lockerd Thomaz 

David Thompson 

Paul Thompson 

Blair Thornton 

Ivar Thorson 

Benoit Thuilot 

Jiang Tian 

David Tick 

Szu-Chi Tien 

Yung Ting 

Gian Diego Tipaldi 

Ethan Tira-Thompson 

Rashi Tiwari 

David Tlalolini 

Andreas Tobergte 

Selene Tognarelli 

Juan Marcos Toibero 

Pratap Tokekar 

Michael Thomas Tolley 

Federico Tombari

Masahiro Tomono 

Lachlan Toohey 

Elin Anna Topp 

Carme Torras 

Abril Torres 

Fernando Torres 

Eduardo Torres-Jara 

Alexander Toshev 

Iwaki Toshima 

David S. Touretzky 

Marc Toussaint 

Benjamin Tovar 

Panos Trahanias 

Duc Trong Tran 

Aksel Andreas Transeth 

Alberto Traslosheros 

Alberto Traslosheros-M 

Peter Trautman 

V. Javier Traver 

Ana Luisa Trejos 

Philip Tresadern 

Alberto Trevisani 

Vito Trianni 

George Triantafyllou 

Jean Triboulet 

Rudolph Triebel 

Marco Trincavelli 

Chiara Troiani 

Nikolaos Tsagarakis 

Chia-Hung Tsai 

Dimitris Tsakiris 

Nikolaos Tsekos 

Panagiotis Tsiotras 

Manolis Tsogas 

John Tsotsos 

Antonios Tsourdos 

Sadayuki Tsugawa 

Toshiaki Tsuji 

Hideyuki Tsukagoshi 

Yuichi Tsumaki 

Mihran Tuceryan 

Stephen Tully 

Win Tun Latt 

Alessio Turetta 

Masaru Uchiyama 

Ryuichi Ueda 

Mitsunori Uemura 

Hiroshi Ueno 

Toshio Ueshiba 

Tsuyoshi Ueyama 

Emre Ugur 

Barkan Ugurlu 

Klaus Uhl 

Heinz Ulbrich 

Giovanni Ulivi 

Paul Umbanhowar 

James Patrick Underwood 

Alois Unterholzner 

Ben Upcroft 

Takateru Urakubo 

Alfonso Urquia 

Nobuhiro Ushimi 

Yuzuko Utsumi 

Nikolaus Vahrenkamp 

Ravi Vaidyanathan 

Pablo Valdivia y Alvarado 

Philip Valencia 

Luis M. Valentin-Coronado 

Alberto Valero-Gomez 

Heike Vallery 

Jaime Valls Miro 

Marco Valtorta 

Michaël Van Damme 

M.J.G. van de Molengraft 

Michiel van de Panne 

Joop van de Ven 

Jur van den Berg 

Ferdi van der Heijden 

H.F. Machiel Van der Loos 

Frank van der Stappen 

Ronald Van Ham 

Joshua Vander Hook 

Bram Vanderborght 

Paul Varnell 

Pablo Varona 

Panagiotis Vartholomeos 

Francisco Vasconcelos 

Eric Vasselin 

Pascal Vasseur 

Gabriele Vassura 

Shrihari Vasudevan 

Monica Vatteroni 

Richard Vaughan 

Marynel Vazquez 

Harini Veeraraghavan 

Prasanna Velagapudi 

Matteo Venanzi 

Marilena Vendittelli 

Jan Veneman 

Subramaniam Venkatraman 

Rodrigo Ventura 

Gentiane Venture 

Kevin Veon 

Alexander Verl 

Paul Vernaza 

John Vial 

Janis Viba 

Marco Vicentini 

Alessandro Correa Victorino 

Rene Vidal 

Fernando Vidal-Verdú 

José Vieira 

Diego Viejo 

Pierre Vieyres 

Bjørnar Vik 

Vishesh Vikas 

Carlos Villalpando 

Michael Villamizar Vergel 

Luigi Villani 

Markus Vincze 

Stephane Viollet 

Francesco Visentin 

Arnoud Visser 

Michael Vistein 

Nicola Vitiello 

Valentina Vitiello 

Marie-Aude Vitrani 

Iason Vittorias 


–xx– 

Michael Vitus 

Kostas Vlachos 

Christopher Vo 

Luca Vollero 

Marsette Vona 

Randolph Voorhies 

Philipp Vorst 

Stavros Vougioukas 

Trung-Dung Vu 

Sven Wachsmuth 

Masayoshi Wada 

Neeti Wagle 

Glenn Wagner 

Mattias Wahde 

Friedrich M. Wahl 

Keith Wait 

Shuichi Wakimoto 

Ian Walker 

Andrew M. Wallace 

Jennifer Walter 

Matthew Walter 

Michael Leonard Walters 

Chaoli Wang 

Chieh-Chih Wang 

Danwei Wang 

David Wang 

Fei-Yue Wang 

Han Wang 

Hao Wang 

Hesheng Wang 

Hongbo Wang 

Huadong Wang 

Hui Wang 

Huifang Wang 

Jianxun Wang 

Jing Wang 

Jingguo Wang 

Jiuguang Wang 

Junping Wang 

Kai Wang 

Kundong Wang 

Long Wang 

Lu Wang 

Meimei Wang 

Nianfeng Wang 

Qining Wang 

Shuguo Wang 

Shuo Wang 

Wei-Wen Wang 

Weifu Wang 

Wenhui Wang 

X. Rosalind Wang 

Xinqing Wang 

Yao-Dong Wang 

Yue Wang 

Zheng Wang 

Zhidong Wang 

Zhifeng Wang 

Zhijie Wang 

Zhongkui Wang 

Tim Wark 

Steven Lake Waslander 

John Wason 

Mutsumi Watanabe 

Tetsuyou Watanabe 

Jens Wawerla 

Stephen Leslie Scott Webb 

Sarah E. Webster 

Robert James Webster III 

Hongxing Wei 

Andrew P.H. Weightman 

Felipe A. Weilemann Belo 

Stephan Weiss 

Alfredo Weitzenfeld 

Uchechukwu C. Wejinya 

Kai Welke 

Briana Wellman 

Johannes Wendeberg 

Philippe Wenger 

Patrick Wensing 

Justin Werfel 

Moritz Werling 

E.P. Westebring – v.d. Putten 

Nicholas Wettels 

David Wettergreen 

Paul White 

Jacob Whitehill 

Eric Whitman 

Mark Albert Whitty 

Pierre-Brice Wieber 

Nicholas Wilkinson 

Volker Willert 

Richard Willgoss 

Aaron L. Williams 

Brian Patrick Williams 

Ian Williams 

Kjerstin Williams 

Mary-Anne Williams 

Stefan Bernard Williams 

Stephen Williams 

Reg Willson 

Thomas Wimboeck 

Alan Winfield 

Alexander Winkler 

Amos Greene Winter 

Raul Wirz Gonzalez 

Heinz Woern 

Walter Wohlkinger 

Hans Woithe 

Michael Wolf 

Sebastian Wolf 

Bryn Wolfe 

Carsten Wolff 

Dirk Wollherr 

Lok Sang Lawson Wong 

Tichakorn Wongpiromsarn 

John Wood 

Nathan Wood 

Robert Wood 

Oliver Woodford 

Oliver Woodman 

Matthew Woodward 

Craig Woolsey 

James Worcester 

Franz Wotawa 

Britta Wrede 

Sebastian Wrede 

Ban Wu

Bing-Fei Wu 

Chao Wu 

Guanglei Wu 

Guosheng Wu 

Haiyuan Wu 

Huapeng Wu 

Jianxin Wu 

Po Wu 

Shandong Wu 

Wencen Wu 

Burkhard Wuensche 

Kai M. Wurm 

Gordon Wyeth 

Christian Wögerer 

FengFeng Xi 

Tian Xia 

Zhiyu Xiang 

Jizhong Xiao 

Jun Xiao 

Shunli Xiao 

Ming Xie 

Shaorong Xie 

Anqi Xu 

Bin Xu 

Changhai Xu 

De Xu 

Jijie Xu 

Kai Xu 

Qingsong Xu 

Tao Xu 

Yang Xu 

Yangsheng Xu 

Yiliang Xu 

Yunfei Xu 

Zhe Xu 

Zhixing Xue 

Takehisa Yairi 

Hiroya Yamada 

Katsuhiko Yamada 

Takayoshi Yamada 

Hiroaki Yamaguchi 

Masaki Yamakita 

Ikuo Yamamoto 

Motoji Yamamoto 

Yoko Yamanishi 

Mitsuhiro Yamano 

Natsuki Yamanobe 

Atsushi Yamashita 

Hiromasa Yamashita 

Junji Yamato 

Brian Yamauchi 

Tasuku Yamawaki 

Kimitoshi Yamazaki 

Gangfeng Yan 

Rujiao Yan 

Holly Yanco 

Chenguang Yang 

Guilin Yang 

Huizhen Yang 

Junho Yang 

Ming Yang 

Ruiguo Yang 

Sungwook Yang 

Tao Yang 

Woosung Yang 

Xiaoli Yang 

Yousheng Yang 

Zhenwang Yao 

Masahito Yashima 

Sergey Yatsun 

Jano Yazbeck 

M. J. Yazdanpanah 

Changlong Ye 

Hsin-Yi (Cindy) Yeh 

Je Sung Yeon 

Dmitry Yershov 

Anna Yershova 

Byung-Ju Yi 

Jianqiang Yi 

Jingang Yi 

Chih-Chen Yih 

Mark Yim 

Sehyuk Yim 

KangKang Yin 

John David Yoder 

Kazuhito Yokoi 

Yasuyoshi Yokokohji 

Naokazu Yokoya 

Kan Yoneda 

Hyun-Soo Yoon 

Jungwon Yoon 

Yong-San Yoon 

Eiichi Yoshida 

Morio Yoshida 

Takami Yoshida 

Kenji Yoshigoe 

Kazuyoshi Yoshii 

Tomoaki Yoshikai 

Taizo Yoshikawa 

Kitaro Yoshimitsu 

Jason Yosinski 

Kuu-young Young 

Shelley S.C. Young 

Ryuh Youngsun 

Karim Youssef 

Hongnian Yu 

Huili Yu 

Hung-Hsiu Yu 


–xxi– 

Jiancheng Yu 

Jingjin Yu 

Jingjun Yu 

Ningbo Yu 

Qian Yu 

Shengwei Yu 

Son Cheol Yu 

Yong Yu 

Yue-Qing Yu 

Chunrong Yuan 

Jianjun Yuan 

Peijiang Yuan 

Dongwon Yun 

Seung-kook Yun 

Kris Zacny 

Spyros Zafeiropoulos 

Michael Friedrich Zäh 

Nazanin Zaker 

Alexey Zakharov 

Avideh Zakhor 

Eduardo Zalama 

Andrea Maria Zanchettin 

Damiano Zanotto 

René Zapata 

Michael M. Zavlanos 

Massimiliano Zecca 

Milos Zefran 

Said Zeghloul 

Garth J Zeglin 

John S. Zelek 

Andreas Zell 

Hendrik Zender 

Shuqing Zeng 

Wenwu Zeng 

Davide Zerbato 

Chengkun Zhang 

Dongjun Zhang 

Guoxuan Zhang 

Haihong Zhang 

Hong Zhang 

Hong Zhang 

Houxiang Zhang 

Jian Zhang 

Jiangbo Zhang 

Jianwei Zhang 

Junjie Zhang 

Li Zhang 

Li Zhang 

Liang Zhang 

Liangjun Zhang 

Mingjun Zhang 

Shiqi Zhang 

Tao Zhang 

Weizhong Zhang 

Wenzeng Zhang 

Xianmin Zhang 

Xuping Zhang 

Yan Zhang 

Yan Liang Zhang 

Yanwu Zhang 

Ying Zhang 

Yizhai Zhang 

Yong Zhang 

Yongsheng Zhang 

Yu (Tony) Zhang 

Yunong Zhang 

Zhao Zhang 

Dongbin Zhao 

Huijing Zhao 

Jianguo Zhao 

Jing Zhao 

Mingguo Zhao 

Tao Zhao 

Nanning Zheng 

Xiaoming Zheng 

Yu Zheng 

Debao Zhou 

Kai Zhou 

Lelai Zhou 

Quan Zhou 

Xiaobo Zhou 

Xun Zhou 

Yan Zhou 

Yu Zhou 

Zhi Zhou 

Wen-Hong Zhu 

Zhiwei Zhu 

Stefan Zickler 

Julius Ziegler 

Marc Ziegler 

Michael Zillich 

Primo Zingaretti 

Michael Zinn 

Zoran Zivkovic 

Alessandro Antonio Zizzari 

Robert Zlot 

M.H. Zokaei Ashtiani 

Johann Marius Zöllner 

Raoul Zöllner 

Loredana Zollo 

Qingze Zou 

Wei Zou 

Matthew Zucker 

Zhiyuan Zuo 

Matthijs Jan Zwinderman

Plenary Sessions 

Plenary I: Design 

Tuesday, September 27, 2011, 12:30-13:45, Continental Ballroom 

Moderator: Professor Bernard Roth, Stanford University 

The Design Plenary features three design pioneers (Hirose, Hirzinger, and Raibert) 

discussing their perspectives on various aspects of mechanism design. After initial 

presentations by the panelists, the chair (Roth) will moderate a panel discussion with the 

experts and the audience. 

Professor Shigeo Hirose was born in Tokyo in 1947. He received his B.Eng. 

Degree with First Class Honors in Mechanical Engineering from Yokohama 

National University in 1971, and his M. Eng. and Ph.D. Eng. Degrees in 

Control Engineering from Tokyo Institute of Technology in 1973 and 1976, 

respectively. From 1976 to 1979 he was a Research Associate, and from 

1979 to 1992 an Associate Professor. Since 1992 he has been a Professor 

in the Department of Mechanical and Aerospace Engineering at the Tokyo 

Institute of Technology. He is a fellow of JSME and IEEE. He is engaged in creative design 

of robotic systems. Prof. Hirose has been awarded more than twenty prizes. 

Professor Gerd Hirzinger is director at DLR’s institute for „Robotics and 

Mechatronics“, which is one of the biggest and most acknowledged 

Institutes in the field worldwide. He was prime investigator of the space robot 

technology experiment ROTEX, the first remote controlled robot in space, 

which flew onboard shuttle COLUMBIA in April 1993. He has published 

more than 600 papers in robotics. He received numerous national and 

international awards, e.g., in 1994 the Joseph-Engelberger-Award for 

achievements in robotic science and in 1995 the Leibniz-Award, the highest scientific 

award in Germany and the JARA (Japan robotics association) Award. In 2005 he received 

the IEEE Pioneer Award of the Robotics and Automation Society, and in 2007 the IEEE 

Field Award “Robotics and Automation”. 

Dr. Marc Raibert was Professor of Electrical Engineering and Computer 

Science at MIT and a member of the Artificial Intelligence Laboratory from 

1986 through 1995. He is co-founder and President of Boston Dynamics Inc, 

(BDI), which is located near MIT in Cambridge. Raibert's research is devoted 

to the study of systems that move dynamically, including physical robots and 

animated creatures. Raibert received a B.S. degree in Electrical Engineering 

from Northeastern University in 1973, and a Ph.D from the Massachusetts 

Institute of Technology in 1977. He is author of Legged Robots That Balance published by 

MIT Press, and is on the Editorial Board of the International Journal of Robotics Research. 

He is a fellow of the AAAI. 


–xxii–

Plenary II: BioRobotics 

Wednesday, September 28, 2011, 12:30-13:45, Continental Ballroom 

Moderator: Professor Ruzena Bajcsy, University of California, Berkeley 

The BioRobotics Plenary features three biorobotics pioneers (Berthoz, Buelthoff, and 

Srinivasan) discussing their perspectives on various aspects of biorobotics and biomimetic 

robotics. After initial presentations by the panelists, the chair (Bajcsy) will moderate a panel 

discussion with the experts and the audience. 

Professor Alain Berthoz was born in 1939. He became Civil Engineer at 

École des Mines in 1963, Ph.D. in 1973. As researcher at (CNRS) (1966- 

1981), he established and coordinated the Neurosensory Physiology 

Laboratory (1981-1993). Since 1993, he is professor at Collège de France 

and director of UMR CNRS/Collège de France "Physiology of perception 

and action". He has over 200 scientific publications in international journals 

on physiology of sensori-motor functions and more specifically on the 

oculomotor system, the vestibular system, balance control, and movement perception. He 

had given about 90 invited talks across the world. 

Professor Heinrich Bülthoff is scientific member of the Max Planck 

Society and director at the Max Planck Institute for Biological Cybernetics 

in Tübingen. He is head of the Department Human Perception, Cognition 

and Action in which a group of about 70 researchers investigate 

psychophysical and computational aspects of higher level visual processes 

in object and face recognition, sensory-motor integration, spatial cognition, 

and perception and action in virtual environments. He holds a Ph.D. degree 

in the natural sciences from the Eberhard-Karls-Universität in Tübingen. He was Assistant, 

Associate and Full Professor of Cognitive Science at Brown University in Providence from 

1988-1993 before becoming director at the Max Planck Institute for Biological Cybernetics. 

Professor Mandyam Srinivasan is at the Queensland Brain Institute and 

the School of Information Technology and Electrical Engineering of the 

University of Queensland. He holds an undergraduate degree in Electrical 

Engineering from Bangalore University, a Master's degree in Electronics 

from the Indian Institute of Science, a Ph.D. in Engineering and Applied 

Science from Yale University, a D.Sc. in Neuroethology from the Australian 

National University, and an Honorary Doctorate from the University of 

Zurich. Among his awards and honors are Fellowships of the Australian Academy of 

Science, of the Royal Society of London, and of the Academy of Sciences for the 

Developing World, an Inaugural Federation Fellowship, the 2006 Australia Prime Minister’s 

Science Prize, and the 2008 U.K. Rank Prize for Optoelectronics. 


–xxiii–

Plenary III: Self-Driving Cars 

Thursday, September 29, 2011, 13:30-14:30, Continental Ballroom 

Professor Sebastian Thrun, Stanford University/Google 

Abstract: Most of us use a car every day. But unlike airplanes, which have been flying on 

autopilots for decades, cars are still driven manually - just the way they were driven 100 

years ago. This talk will introduce the transformative concept of a self-driving car. Following 

early research in the 1990 in Germany and the US, and more recently the DARPA 

Challenges, this technology has now been advanced to a point where it is within reach of 

commercial realizations that may provide benefits to pretty much anyone who drives a car. 

At the core of this progress is a new generation of cutting-edge artificial intelligence, which 

enables a self-driving car to understand its environment and to interact with other traffic. 

The speaker will discuss the Google Self-Driving Car project, in which a fleet of self-driving 

cars navigated more than 160,000 miles on public roads in California and Nevada, 

including the downtowns of San Francisco and Los Angeles. The speaker will also discuss 

some of the societal implications of this new technology. 

Sebastian Thrun is a Professor of Computer Science at Stanford 

University and director of the Stanford Artificial Intelligence Laboratory 

(SAIL). He led the development of the robotic vehicle Stanley that won 

the 2005 DARPA Grand Challenge. His team also developed Junior, 

which placed second at the DARPA Urban Challenge in 2007. Thrun led 

the development of the Google self-driving car and is well known for his 

work on probabilistic programming techniques in robotics, with 

applications including robotic mapping. He was elected into the National 

Academy of Engineering and also into the German Academy of Sciences 

Leopoldina in 2007. In 2011, he received the Max-Planck-Research Award and the 

inaugural AAAI Ed Feigenbaum Prize. Thrun received his Diplom (master's degree) in 

1993 and a PhD (summa cum laude) in 1995 in computer science and statistics from the 

University of Bonn. He was on the CMU faculty from 1995 till 2003. Since 2003 he has 

been on the faculty of the Stanford Computer Science department. He is also a Google 

Fellow. 


–xxiv–

Workshops and Tutorials 

A total of 27 workshops and tutorials are scheduled on Sunday, Monday, and Friday of the 

conference week. They are listed here for your consideration. More detailed information 

and all workshops and tutorials proceedings can be found in the conference website 

www.iros2011.org/workshops-and-tutorials. All events take place in the Continental Ball 

and Parlor rooms 1–9, and in the Golden Gate Rooms 6 and 7. 

Sunday, September 25, 2011: Full-day workshops and tutorials 

Session Room Title and Website Link Organizers 

ST1 

Tutorial 

ST2 

Tutorial 

ST3 

Tutorial 

SW4 

Workshop 

SW5 

Workshop 

SW6 

Workshop 

SW7 

Workshop 

SW8 

Workshop 

SW9 

Workshop 

Golden 

Gate 

Rooms 6-7 

Continental 

Parlor 1 

Continental 

Parlor 2 

Continental 

Ballroom 4 

Continental 

Ballroom 6 

Continental 

Parlor 3 

Continental 

Parlor 7 

Continental 

Parlor 8 

Continental 

Parlor 9 

Motion Planning for Real Robots 

3D Point Cloud Processing: PCL (Point 

Cloud Library) 

DARwIn-OP: An Open Platform, Miniature 

Humanoid Robot Platform for Research, 

Education and Outreach 

20 years of Microrobotics: progress, 

challenges, and future directions 

Cognitive Neuroscience Robotics 

Image-Guided Medical Robotic 

Interventions 

Knowledge Representation for 

Autonomous Robots 

Autonomous Underwater Robotics for 

Intervention 

Reconfigurable Modular Robotics: 

Challenges of Mechatronic and Bio- 

Chemo- Hybrid Systems 


–xxv– 

Sachin Chitta 

Edward Gil Jones 

Ioan Alexandru Sucan 

Mark Moll* 

Lydia Kavraki 

Radu Bogdan Rusu* 

Michael Dixon 

Aitor Aldoma 

Suat Gedikli 

Dennis Hong* 

Daniel D. Lee 

Dan Popa* 

Fumihito Arai 

Kenichi Narioka* 

Yukie Nagai 

Minoru Asada 

Hiroshi Ishiguro 

Sarthak Misra* 

Jaydev P. Desai 

Michael Beetz 

Rachid Alami 

Joachim Hertzberg 

Alessandro Saffiotti 

Moritz Tenorth* 

Junku Yuh 

Giuseppe Casalino 

Alessio Turetta* 

Serge Kernbach* 

Robert Charles Fitch

Monday, September 26, 2011: Half-day workshops and tutorials 


MT1 

Tutorial 

MW2 

Workshop 

MW3 

Workshop 

MW4 

Workshop 

MW5 

Workshop 

MW6 

Workshop 

MW7 

Workshop 

MW8 

Workshop 

MW9 

Workshop 

Continental 

Ballroom 5 

Continental 

Parlor 2 

Continental 

Parlor 8 

Continental 

Parlor 1 

Continental 

Parlor 3 

Continental 

Parlor 7 

Continental 

Ballroom 4 

Continental 

Ballroom 6 

Continental 

Parlor 9 

Introduction to Rescue Robotics Robin Murphy* 

Visual Control of Mobile Robots 

(ViCoMoR) 

New and Emerging Technologies in 

Assistive Robotics 

Visual Tracking and Omni-directional 

Vision 

Space Robotics Simulation 

Current Directions in Marine Vehicle 

Autonomy Research 

Active Semantic Perception and Object 

Search in the Real World 

Sixth International Cognitive Vision 

Workshop (ICVW 2011) Situated Vision vs. 

Internet Vision 

Redundancy in Robot Manipulators and 

Multi-Robot Systems 


–xxvi– 

Youcef Mezouar 

G. Lopez-Nicolas* 

Kazuyoshi Wada* 

Machiel Van der Loos 

Loredana Zollo 

El Mustapha Mouaddib* 

Eric Marchand 

João P. Barreto 

Yasushi Yagi 

Rainer Krenn* 

Yves Gonthier 

Donald Eickstedt* 

Mae Seto 

Alper Aydemir 

Andrzej Pronobis 

Bhaskara Marthi 

Patric Jensfelt* 

Dirk Holz 

Barbara Caputo* 

Fiora Pirri 

Michael Zillich 

Dejan Milutinovic* 

Jacob Rosen

Friday, September 30, 2011: Full-day workshops and tutorials 


FW1 

Workshop 

FW2 

Workshop 

FW3 

Workshop 

FW4 

Workshop 

FW5 

Workshop 

SW6 

Workshop 

FW7 

Workshop 

FW8 

Workshop 

FW9 

Workshop 

Continental 

Ballroom 4 

Continental 

Ballroom 6 

Continental 

Parlor 1 

Continental 

Parlor 3 

Continental 

Parlor 7 

Continental 

Parlor 9 

Continental 

Parlor 2 

Continental 

Parlor 8 

Continental 

Ballroom 5 

* = Contact organizer 

European Efforts in Strengthening the 

Academia-Industry Collaboration 

Progress and Open Problems in Motion 

Planning 

Current and Future Related Technologies 

for Robotic Automation in Micro/Nano 

Scale 

Metrics and Methodologies for 

Autonomous Robot Teams in Logistics 

(MMART-LOG) 

Methods for Safer Surgical Robotics 

Procedures 

Robotics for Neurology and Rehabilitation 

Robotics for Environmental Monitoring 

Perception and Navigation for 

Autonomous Vehicles in Human 

Environment 

The PR2 Workshop: Results, Challenges 

and Lessons Learned in Advancing 

Robotics With a Common Platform 


–xxvii– 

Reinhard Lafrenz* 

Alois Knoll 

Bruno Siciliano 

Rainer Bischoff 

Anne Wendel 

Timothy Bretl* 

Dan Halperin 

Kostas E. Bekris 

Masahiro Nakajima* 

Toshio Fukuda 

Alexander Kleiner* 

Rolf Lakaemper 

Raj Madhavan 

Rainer Konietschke* 

Stefan Joerg 

Paolo Fiorini 

Gentiane Venture* 

Philippe Fraisse 


Thierry Keller 

Lino Marques* 

Ryan Smith 

Volkan Isler 

Philippe Martinet* 

Christian Laugier 

Urbano Nunes 

William Smart* 

Sachin Chitta 

Caroline Pantofaru 

Radu Bogdan Rusu

Floorplan 

Demonstration Sessions 

Robotic demonstrations will be shown on the exhibition floor at the Hilton hotel (Golden Gate 

Room 8, Lobby level), next to the interactive sessions. 

Schedule 

Demonstrations run in parallel to the technical sessions from Tuesday, September 27 to 

Thursday morning, September 29, 2011. Demonstrations marked with a star (*) have 

been selected for presentation in the special demonstration symposium (see next table). 


–xxviii–

Monday, Sept 26 Tuesday, Sept 27 Wednesday, Sept 28 Thursday, Sept 29 

Demo Symposium Morning Afternoon Morning Afternoon Morning Afternoon 

Space A 

SP5 

"Teleoperation" 

(Care-o-bot) 

SP3* 

"Service 

robot" 

(Care-o-bot) 

SP4* 

"Active 

perception" 

(Care-o-bot) 

SP6* 

"Mobile 

manipulation" 

(Care-o-bot) 

OR4* 

"Pixhawk 

MAV" 

Space B 

SP7* SP9 SP10* 

"iTaSC "Manipulation "Haptic 

framework" tools" coupling" 

(PR2) (PR2) (PR2, youBot) 

OR9 

"Ballbot 

Rezero" 

OR8 

"The r-one" 

Space C 

OR6* 

"Bio-inspired 

humanoid" 

OR7 

"Needle 

steering" 

OR10 

"Perception 

for 

manipulation" 

OR5 

"Haptic 

rendering" 

SP12* 

"More 

cowbell" 

(Nao) 

Space D 

OR3 

"DARPA 

Arm" 

OR2* 

"Display- 

Swarm" 

OR1* 

"Kilobot" 

SP13 

"Visual 

servoing" 

(Nao) 

SP11* 

"Climbing 

stairs" 

(Nao) 

Space E 

iCub space 

SP1 

"Cooperative 

game" 

(iCub) 

List of Demonstrations 

SP2* 

"Aquila" 

(iCub) 

Standard Platform Demonstrations 

Other iCub 

demos 


–xxix– 

SP8 

"Do not touch" 

(PR2) 

Other iCub 

demos 

Other iCub 

demos 

A number of standard platforms for mobility and manipulation in robotics research now exist, 

as both off-the-shelf commercial products, and research platforms with wide distribution. 

Several manufacturers have made available their platforms for demonstration. There are two 

benefits to this arrangement: demonstrators will not have to transport hardware, and the 

demonstrations will show the increasing use of standard hardware as a mean of attacking 

advanced applications. The accepted demonstrations for this session are: 

SP1 – iCub Learning a Cooperative Game through Interaction with his Human Partner 

Stephane Lallee, Ugo Pattacini, Lorenzo Natale, Giorgio Metta, Peter Ford Dominey 

Stem Cell and Brain Research Institute, Bron, France, Italian Institute of Technology, Genoa, Italy 

Platform: iCub 

Abstract: A human can interact with the iCub by pointing and speaking together in order to learn, 

name and play with unknown objects on a table. User can then ask for more complex actions and 

“program” small games by instructing the robot with a shared plan. 

SP2 – Aquila - The Cognitive Robotics Toolkit (*) 

Martin Peniak, Anthony F. Morse 

University of Plymouth, UK 

Platform: iCub 

Abstract: A live demonstration of Aquila, an open source cognitive robotics software toolkit for the 

iCub robot. This demonstration includes modeling sensorimotor learning in child development, 

action acquisition, and robot teleoperation.

SP3 – General Purpose Service Robot (*) 

Nico Hochgeschwender, Jan Paulus, Michael Reckhaus, Frederik Hegger, Christian A. Mueller, 

Sven Schneider, Paul G. Ploeger, Gerhard K. Kraetzschmar 

Bonn-Rhein-Sieg University of Applied Sciences, Bonn, Germany 

Platform: Care-O-Bot 3 

Abstract: In our demonstration we show a general purpose service robot which performs various 

tasks on demand. 

SP4 – Active Perception Planning on the Care-o-Bot platform (*) 

Robert Eidenberger, Michael Fiegert, Georg von Wichert, Gisbert Lawitzky 

Siemens, Munich, Germany 


Abstract: The Care-O-Bot platform localizes household objects in complex environments and 

grasps them. Environment perception and active perception planning is demonstrated and used for 

efficient scene modeling. 

SP5 – Semi-autonomous Tele-Operation Interface for Robotic Fetch and Carry Tasks 

Renxi Qiu 1 , Alexandre Noyvirt 1 , Nayden Chivarov 2 , Rafael Lopez 3 , Georg Arbeiter 4 , Dayou Li 5 

1 2 3 

Cardiff Univ., UK; Bulgarian Academy of Science, Bulgaria; Robotnik Automation, Spain; 

4 5 

Fraunhofer IPA, Germany; Univ. of Bedfordshire, UK 


Abstract: The demonstration focuses on remote user interface such as Apple IPAD to tele-operate 

robot semi-autonomously. It enables non-expert users taking charge of the robot around the home. 

SP6 – Using Mobile Manipulation to solve a Household Task (*) 

Florian Weisshardt, Alexander Bubeck, Jan Fischer, Ulrich Reiser 

Fraunhofer IPA, Stuttgart, Germany 


Abstract: The task of serving objects is significantly scalable and reaches from table top 

manipulation of simple objects to retrieving objects that require mobile manipulation of the 

environment first. 

SP7 – Demonstration of iTaSC as a unified framework for task specification, control, and 

coordination for mobile manipulation (*) 

Dominick Vanthienen, Tinne De Laet, Markus Klotzbuecher, Ruben Smits, Wilm Decré, Koen Buys, 

Steven Bellens, Herman Bruyninckx, and Joris De Schutter 

Katholieke Universiteit Leuven, Belgium 

Platform: PR2 

Abstract: Mobile co-manipulation task of a PR2 and a human, to illustrate the potential of the iTaSC 

method and its Orocos implementation to specify sensor-based, multi-frame, partially-specified 

robot tasks. 

SP8 – Please Do Not Touch the Robot 

Joseph M. Romano, Katherine J. Kuchenbecker 

University of Pennsylvania, Philadelphia, USA 

Platform: PR2 

Abstract: Natural human-robot interaction requires a tight coupling between sensing and control. 

Our PR2 demo use high-bandwidth tactile signals to showcase dynamic interactive behaviors such 

as high-fives. 


–xxx–

SP9 – Interactive Manipulation Tools for the PR2 Robot 

Matei Ciocarlie, Kaijen Hsiao 

Willow Garage, Menlo Park, USA 

Platform: PR2 

Abstract: Interactive manipulation tools for the PR2: everything from low-level tools such as moving 

grippers around in Cartesian space, to high-level tools such as grasping segmented or recognized 

objects. 

SP10 – Haptic coupling with augmented feedback between the KUKA youBot and the PR2 

robot arms (*) 

Steven Bellens, Koen Buys, Nick Vanthienen, Tinne De Laet, Ruben Smits, Markus Klotzbuecher, 

Wilm Decré, Herman Bruyninckx, and Joris De Schutter 


Platform: PR2 and youBot 

Abstract: We demonstrate haptic coupling between the KUKA youBot and the PR2 robot arms. A 

wearable display provides the operator with PR2 camera images, his head movement is coupled 

with the PR2's head. 

SP11 – Detecting and Climbing Stairs with a Laser-equipped Nao Humanoid (*) 

Stefan Osswald, Armin Hornung, Maren Bennewitz 

University of Freiburg, Germany 

Platform: Nao 

Abstract: We demonstrate autonomous stair climbing based on laser and vision data. After 

collecting a 3D laser scan, Nao detects steps of a staircase and climbs the staircase using visual 

information. 

SP12 – More cowbell! A musical ensemble with the NAO thereminist (*) 

Angelica Lim, Takeshi Mizumoto, Takuma Otsuka, Tatsuhiko Itohara, Kazuhiro Nakadai 1,2 , Tetsuya 

Ogata, Hiroshi G. Okuno 

Kyoto University; 1 Honda Research Institute, Wako, Japan; 2 Tokyo Institute of Technology, Japan 

Platform: Nao 

Abstract: We present the first live performance of our interactive music robot: Nao plays the 

theremin and listens to humans to stay in sync. Demo attendees are invited to join by playing 

cowbell or maracas. 

SP13 – Model-based Visual Servoing Tasks on Nao Robot 

A. Abou Moughlbay 1 , J. J. Sorribes 2 , E. Cervera 2 , P. Martinet 1 

1 

LASMEA, Blaise Pascal University, France 

2 

RobInLab, Jaume-I University, Spain 

Platform: Nao 

Abstract: Model-based visual servoing is implemented on the Nao robot. Based on geometric 

models, the robot performs localization, tracking and grasping of objects in a semi-structured 

environment. 

Open Research Demonstrations 

The goal of the open research demonstration session is to give participants an open forum 

to present their latest developments on robotic systems and software. The accepted 

demonstrations for this session are: 


–xxxi–

OR1 – Kilobot: A Low Cost Scalable Robot System for Collective Behaviors (*) 

Michael Rubenstein, Radhika Nagpal 

Harvard University, Cambridge, USA 

Abstract: The Kilobot is a low cost, easy to use robot, which is designed for testing swarm 

algorithms on hundreds to thousands of robots. We will demo robot operations, and sample 

behaviors on 100 Kilobots. 

OR2 – DisplaySwarm: A robot swarm displaying images (*) 

Javier Alonso-Mora 1,2 , Andreas Breitenmoser 1 , Martin Rufli 1 , Stefan Haag, Gilles Caprari 3 , Roland 

Siegwart 1 , Paul Beardsley 2 

1 ETH Zurich; 2 Disney Research Zurich; 3 CGTronic Lugano, Switzerland 

Abstract: DisplaySwarm represents images in a novel way using small mobile robots with 

controllable colored illumination. The work addresses research questions in collision avoidance and 

pattern formation. 

OR3 – The DARPA ARM Robot: Available at Your Desk 

Gill Pratt 1 , Jim Pippine 2 , Andrew Mor 3 , Natalie Salaets 4 

1 DARPA DSO; 2 Golden Knight Technologies; 3 RE2, 4 System Planning Corporation, USA 

Abstract: The DARPA ARM demonstration features both the publicly available ARM robot simulator 

and a real remote robot. Visitors interact with the simulator to perform various tasks, then watch the 

real remote robot do the same tasks. 

OR4 – Flying the Pixhawk MAV - A Computer Vision Controlled Quadrotor (*) 

Lorenz Meier, Petri Tanskanen, Lionel Heng, Gim-Hee Lee, Friedrich Fraundorfer, Marc Pollefeys 

ETH Zurich, Switzerland 

Abstract: The Pixhawk micro air vehicle is an autonomous flying robot equipped with cameras and 

onboard computer. It will fly based on onboard visual localization and perform the task of object 

tracking and pattern recognition. 

OR5 – Proxy Method for Fast Haptic Rendering from Time Varying Point Clouds 

Fredrik Ryden, Sina Nia Kosari, Howard Jay Chizeck 

University of Washington, Seattle, USA 

Abstract: This demonstration will allow users to directly “feel” physical objects imaged by a Kinect. 

They will be able to directly experience haptic rendering of both fixed and moving objects in real-time. 

OR6 – Bio-inspired vertebral column, compliance and semi-passive dynamics in a 

lightweight humanoid robot (*) 

Olivier Ly, Matthieu Lapeyre, Pierre-Yves Oudeyer 

LABRI-INRIA, Talence, France 

Abstract: We demonstrate the use of compliance and vertebral column in humanoid locomotion. 

OR7 – A Robotic System for Needle Steering 

Ann Majewicz 1 , John Swensen 2 , Tom Wedlick 2 , Kyle Reed 3 , Ron Alterovitz 4 , Vinutha Kallem 5 , 

Wooram Park 6 , Animesh Garg, Gregory Chirikjian 2 , Ken Goldberg 7 , Animesh Garg 7 , Noah Cowan 2 , 

and Allison Okamura 1 

1 Stanford Univ.; 2 John Hopkins Univ.; 3 Univ. of South Florida; 4 University of North Carolina at 

Chapel Hill; 5 Univ. of Pennsylvania; 6 Univ. of Texas at Dallas, USA; 7 UC Berkeley; 

Abstract: A live demonstration of robotic needle steering in artificial tissue, as well as videos and 

posters about models and simulations, path planners, controllers, and integration with medical 

imaging. 


–xxxii–

OR8 – The r-one: A Low-Cost Robot for Research, Education, and Outreach 

A. Lynch, J. McLurkin 

Rice University, Houston, USA 

Abstract: We present the r-one: An advanced, low-cost robot for research and education. Come 

see autonomous multi-robot exploration, drive a mini-swarm, and write your program at 

programming stations. 

OR9 – Ballbot Rezero 

Simon Doessegger, Peter Fankhauser, Corsin Gwerder, Jonathan Huessy, Jerome Kaeser, 

Thomas Kammermann, Lukas Limacher, Michael Neunert, Francis Colas, Cedric Pradalier, Roland 

Siegwart 

ETH Zurich, Switzerland 

Abstract: Ballbots are able to balance and drive on a single sphere. Designed for high agility, 

organic motion and human interaction, Ballbot Rezero exploits its inherent, unstable dynamics. 

OR10 – Generic Perception for Manipulation 

Manuel Brucker 1 , Chavdar Papazov 2 , Simon Leonard 3 , Darius Burschka 2 , Gregory D. Hager 3 , Tim 

Bodenmueller 1 

1 DLR, Germany; 2 TU Munich, Germany; 3 John Hopkins University, Baltimore, USA 

Abstract: Our scene parsing algorithm leverages physical constraints to enhance data fitting over 

time and space. Our demonstration will show a robot manipulating complex scenes composed of 

various objects. 

Symposium 

A special symposium, Robot Demonstrations, will highlight twelve of the best demo 

proposals, with short descriptions of the demos by the authors. 

When: Monday, September 26, 2011, 16:00 

Where: Continental Ballroom 4 (Session MoBT4) 

List of talks 

SP2 – Aquila - The Cognitive Robotics Toolkit 

SP3 – General Purpose Service Robot 

SP4 – Active Perception Planning on the Care-o-Bot platform 

SP6 – Using Mobile Manipulation to solve a Household Task 

SP7 – Demonstration of iTaSC as a unified framework for task specification, control, and 

coordination for mobile manipulation 

SP10 – Haptic coupling with augmented feedback between the KUKA youBot and the PR2 robot 

arms 

SP11 – Detecting and Climbing Stairs with a Laser-equipped Nao Humanoid 

SP12 – More cowbell! A musical ensemble with the NAO thereminist 

OR1 – Kilobot: A Low Cost Scalable Robot System for Collective Behaviors 

OR2 – DisplaySwarm: A robot swarm displaying images 

OR4 – Flying the Pixhawk MAV - A Computer Vision Controlled Quadrotor 

OR6 – Bio-inspired vertebral column, compliance and semi-passive dynamics in a lightweight 

humanoid robot 


–xxxiii–

Floorplan 

Exhibition 

The Exhibition venue is the Golden Gate room at the Lobby level of the Hilton hotel. 

Schedule 

Exhibitors will be active during the following days: 

Tuesday, September 27, 2011 9:00 – 17:00 

Wednesday, September 28, 2011 9:00 – 17:00 

Thursday, September 29, 2011 9:00 – noon 


–xxxiv–

Exhibitors 

Platinum Corporate Sponsors exhibiting: 

Bosch Research and Technology Center www.boschresearch.com 

Honda Research Institute www.honda-ri.com/HRI_Us 

KUKA Robotics Corp. www.kuka.com 

SRI International www.sri.com 

Gold Corporate Sponsors exhibiting: 

Willow Garage, Inc. www.willowgarage.com 

Silver Corporate Sponsors exhibiting: 

Aldebaran Robotics www.aldebaran-robotics.com 

Google, Inc. research.google.com 

Intuitive Surgical, Inc. www.intuitivesurgical.com 

Shunk Intek, Inc. www.us.schunk.com 

Additional Exhibitors: 

Adept MobileRobots www.adept.com 

Ascending Technologies, Inc. www.asctec.de 

Barobo, Inc. www.barobo.com 

Barrett Technology, Inc. www.barrett.com 

BioRob GmbH www.biorob.de 

Butterfly Haptics, LLC www.butterflyhaptics.com 

Coroware, Inc. www.coroware.com 

DARPA ARM Outreach www.thearmrobot.com 

Festo Didactic www.festo.us 

Hokuyo Automatic Co., Ltd. www.hokuyo-aut.jp 

John Wiley & Sons www.wiley.com 

Kawada Industries, Inc. www.kawada.co.jp 

National Instruments Corporation www.ni.com 

RoadNarrows Robotics www.roadnarrows.com 

RobotCub Consortium (iCub) www.icub.org 

Robotis, Inc. www.robotis.com 

SimLab Co., Ltd www.simlab.co.kr 

Skybotix AG www.skybotix.com 

Springer Science+Business Media www.springer.com 


–xxxv–

IROS 2011 Corporate Sponsors 

We acknowledge the support of the following Corporate Sponsors to the 2011 IEEE/RSJ 

International Conference on Intelligent Robots and Systems. 

Platinum 

www.boschresearch.com www.honda-ri.com/HRI_Us www.kuka.com www.sri.com 

Gold 

Silver 

www.abb.com/robots www.willowgarage.com 

www.aldebaran-robotics.com research.google.com www.intuitivesurgical.com www.us.schunk.com 

At the end of this Digest, you will find ads from the above Corporate Sponsors. 


–xxxvi–

IROS 2011 Awards 

IROS Harashima Award 

To honor Professor Fumio Harashima, the Honorary Founding Chair of IROS conferences, 

by recognizing outstanding contributions of an individual of the IROS community, who has 

pioneered activities in robotics and intelligent systems. 

NTF Award for Entertainment Robots and Systems 

Started in 2007, this award is to encourage research and development of "entertainment 

robots and systems", and new technologies for future entertainment. Sponsored by the 

New Technology Foundation; Certificate + USD 1,000. 

JTCF Novel Technology Paper Award for Amusement Culture 

This award recognizes practical technology contributing to toys, toy models, and amusement 

culture. Sponsored by the Japan Toy Culture Foundation; Certificate + JPY 100,000. 

RoboCup Best Paper Award 

Sponsored by The RoboCup Federation; Certificate + USD 500. 

IROS CoTeSys Cognitive Robotics Best Paper Award 

Started in 2010, this award is to promote interdisciplinary research on cognition for 

technical systems (CoTeSys) and advancements of cognitive robotics in industry, home 

applications, and daily life. Sponsored by the German Cluster of Excellence, CoTeSys; 

Certificate + USD 1,000. 

Best Application Paper Award 

Sponsored by the Institute of Control, Robotics, and Systems (ICROS); Certificate + USD 

1,000. 

IROS 2011 Best Paper Award 

To recognize the most outstanding paper presented at the Conference, based on the 

quality of the contribution, of the written paper, and of the oral presentation. Sponsored by 

RSJ and SICE; Certificate + USD 2,000. 

IROS 2011 Best Student Paper Award 

To recognize the most outstanding paper authored primarily and presented by a student at 

the Conference, based on the quality of the contribution, of the written paper and of the oral 

presentation. Sponsored by the IROS 2011 Conference; Certificate + USD 1,000. 

All award winners will be announced during the Awards Lunch on Thursday, September 29. 


–xxxvii–

Welcome Reception 

Social Events 

A welcome reception will be offered to all IROS 2011 participants on Monday, 

September 26, 18:30-20:00. After the end of the sessions, we will meet in the Continental 

Ballroom. 

Dinner Cruise on the Bay 

San Francisco's skyline is most spectacular when seen from the water. On Tuesday, 

September 27, we will take you on an afternoon and dinner cruise on the San Francisco 

Belle, a beautiful three-leveled 292-foot sternwheeler with a unique Art Nouveau style. 

During the cruise, you will have ample opportunities to enjoy the views of the City and 

many of its most famous sights, such as Alcatraz, Angel Island, the Embarcadero, 

Fisherman's Wharf, Coit Tower, the skyscrapers of the Financial District, and – of course – 

the Golden Gate Bridge. Transfer busses will leave at the Hilton hotel at 16:00, and we 

expect to be back at the hotel at 22:00. 

Gold Lunch 

On Wednesday, September 28, at noon, a lunch for all Graduates of the Last Decade 

(GOLD) will be organized at the Continental Ballroom level. This luncheon was initiated 

within the RAS Technical Activities Board as a mean to let graduates be aware of what the 

society has to offer and to network with each other. The opportunity is also used to present 

the structure of the society and introduce the various Technical Committees forming TAB. 

RAS members will have first priority. If you are not yet a member, you can sign up now at 

www.ieee-ras.org. 

Lunch with Leaders 

This event is organized by the IEEE Robotics and Automation Society (RAS) and will take 

place in parallel to the GOLD lunch on Wednesday, September 28, at noon. Lunch with 

Leaders (LwL) was initiated by the Student Activities Committee with the aim to provide 

students with an opportunity to get in contact with leaders and get advice and mentoring on 

their career and research. RAS members will have first priority. 

Evening at the Museum with Picasso 

This year, we have discovered a very special venue for the conference banquet. It will be 

held on the evening of Wednesday, September 28, in the de Young Museum. The 

museum is situated in Golden Gate Park, which extends all the way to Ocean Beach, and 

which features many other attractions such as the Japanese Tea Garden, the Botanical 

Garden, and the California Academy of Sciences, to name but a few. As a special highlight, 

we will have exclusive access to the Picasso Exhibition. Transfer busses will leave at the 

Hilton hotel at 18:00, and we expect to be back at the hotel at 22:00. 


–xxxviii–

Awards Ceremony 

All IROS 2011 participants are invited to attend the Awards Ceremony in the Continental 

Ballroom on Thursday, September 29, at 12:30. All conference participants are welcome 

to join while the eight IROS 2011 Conference Awards will be presented. 

Farewell Reception 

At the end of the conference, a reception will gather all IROS 2011 participants in the 

Continental Ballroom on Thursday, September 29, 18:30-20:00. 


–xxxix–

Map of Conference Venue 


–xl–

Conference Overview 

IROS 2011 features three types of sessions. In addition to the customary technical tracks 

of regular sessions, there are specially organized symposia, novel interactive sessions, 

and five forums. 

Regular Sessions 

These consist of eight papers. Five papers are presented in regular (15 minute) talk format. 

Three papers are allotted short (5 minute) presentations. Each of these “teaser” talks 

previews the corresponding paper and invites the audience to the full presentation of the 

paper at the subsequent (90 minute) interactive session in the special presentation area 

equipped with large screens. 

Symposia 

Symposia are invited sessions that are focused on a specific subfield. Organized by 

renowned researchers, they are a celebration of 50 years of robotics. All symposia consist 

of papers that have undergone the regular review process. In addition, a longer invited talk 

(semi-plenary) is included in some symposia. 

Interactive sessions 

These sessions allow a carefully selected number of papers to be presented in an 

interactive manner on large flat screens. Up to 30 papers are presented in parallel to small 

groups of interested attendees during each session, in order to foster a lively exchange of 

ideas between participants. Before being presented in an interactive session, every such 

paper is previewed in the form of a short (5 minute) “teaser” talk in a regular session. It is 

important to note that papers presented during interactive sessions were reviewed and are 

published exactly as regular papers. The Senior Program Committee selected those 

papers for interactive sessions that are most likely to benefit from the special presentation 

format. 

Industrial Forums 

IROS 2011 features three industrial forums, each one session long. Each forum includes 

representatives from robotics companies making short presentations and engaging in a 

moderated panel discussion among themselves and with the audience. 

Robots: The New Commercial Platforms 

This forum focuses on recent and emerging robotic platforms. Participants from industry 

discuss recent and projected advances in robotic technology with a commentary on 

emerging applications. The forum is moderated by Professor Rüdiger Dillmann. 


–xli–

Robots: The Next Generation 

This forum focuses on next generation robotic platforms, new business models for robotics, 

and the role of open software. Participants from industry discuss advances in robotic 

technology with an emphasis on software and applications. The forum is moderated by 

Steve Cousins. 

Medical Robotics 

This forum focuses on the rapidly growing field of medical robotics. In synergy with the 

Symposia on medical robotics at IROS 2011, this forum features industry experts 

discussing and presenting cutting edge commercial advances in the field. The forum is 

moderated by Professor Paolo Dario. 

Forums 

Robotics: Beyond the Horizon 

In addition to the industrial forums, IROS 2011 features a special ‘Blue Sky’ forum on the 

future of robotics. Participants from academia, government, and industry present their 

visions for the future of the field. The forum is moderated by Professor Hirochika Inoue. 

On Robotics Conferences: A Town Hall Meeting 

IROS 2011 features a town hall meeting forum. The forum is an opportunity for conference 

attendees to provide feedback about the conference, and for future conference organizers 

to briefly present their vision of the meetings they are planning. The forum is moderated by 

Professor Peter Corke. 


–xlii–

IROS 2011 Technical Program at a Glance: Monday, September 26, 2011 

Track T1 Track T2 Track T3 Track T4 Track T5 Track T6 Track T7 Track T8 Track T9 Track T10 

14:00-15:30 

MoAT1 

Continental 

Parlor 1 

Microsensing 

16:00-17:30 

MoBT1 

Continental 

Parlor 1 

Micromanipulation 

14:00-15:30 

MoAT2 

Continental 

Parlor 2 

Localization 

16:00-17:30 

MoBT2 

Continental 

Parlor 2 

Localization with 

Constraints 

14:00-15:30 

MoAT3 

Continental 

Parlor 3 

Symposium: 

Robot Audition: 

Active Audition 

16:00-17:30 

MoBT3 

Continental 

Parlor 3 

Symposium: 

Robot Audition: 

From Sound 

Source 

Localization to 

Automatic Speech 

Recognition 

14:00-15:30 

MoAT4 

Continental 

Ballroom 4 

Symposium: 

Telerobotics 

16:00-17:30 

MoBT4 

Continental 

Ballroom 4 

Symposium: 

Robot 

Demonstrations 

14:00-15:30 

MoAT5 

Continental 

Ballroom 5 

Symposium: 

Bio-Inspired 

Robotics I 

16:00-17:30 

MoBT5 

Continental 

Ballroom 5 

Symposium: 

Bio-Inspired 

Robotics II 


–xliii– 

14:00-15:30 

MoAT6 

Continental 

Ballroom 6 

Symposium: 

Field Robotics I 

16:00-17:30 

MoBT6 

Continental 

Ballroom 6 

Symposium: 

Field Robotics II 

14:00-15:30 

MoAT7 

Continental 

Parlor 7 

Wheeled Robots 

16:00-17:30 

MoBT7 

Continental 

Parlor 7 

Teleoperation 

14:00-15:30 

MoAT8 

Continental 

Parlor 8 

Learning 

Parameterized 

Policies 

16:00-17:30 

MoBT8 

Continental 

Parlor 8 

Learning for 

Control 

14:00-15:30 

MoAT9 

Continental 

Parlor 9 

Novel Actuators I 

16:00-17:30 

MoBT9 

Continental 

Parlor 9 

Novel Actuators II 

16:00-17:30 

MoBPT10 

Golden Gate 

Room 

Interactive I

IROS 2011 Technical Program at a Glance: Tuesday, September 27, 2011 


08:00-09:30 

TuAT1 

Continental 

Parlor 1 

Object 

Recognition, 

Segmentation, 

and Detection 

10:00-11:30 

TuBT1 

Continental 

Parlor 1 

Perception, 

Saliency and 

Novelty 

14:00-15:30 

TuCT1 

Continental 

Parlor 1 

Object Detection 

& Collision 

Avoidance 

08:00-09:30 

TuAT2 

Continental 

Parlor 2 

Simultaneous 

Localization and 

Mapping 

10:00-11:30 

TuBT2 

Continental 

Parlor 2 

Semantic SLAM & 

Loop Closure 

14:00-15:30 

TuCT2 

Continental 

Parlor 2 

Motion Estimation, 

Mapping & SLAM 

08:00-09:30 

TuAT3 

Continental 

Parlor 3 

Symposium: 

Microrobotics 

I 

10:00-11:30 

TuBT3 

Continental 

Parlor 3 

Symposium: 

Microrobotics 

II 

14:00-15:30 

TuCT3 

Continental 

Parlor 3 

Symposium: 

Microrobotics 

III 

08:00-09:30 

TuAT4 

Continental 

Ballroom 4 

Industrial Forum: 

Robots: The New 

Commercial 

Platforms 

10:00-11:30 

TuBT4 

Continental 

Ballroom 4 


Robots: The Next 

Generation 

14:00-15:30 

TuCT4 

Continental 

Ballroom 4 

Forum: 

Robotics: Beyond 

the Horizon 

08:00-09:30 

TuAT5 

Continental 

Ballroom 5 

Symposium: 


I 

10:00-11:30 

TuBT5 

Continental 

Ballroom 5 

Symposium: 


II 


–xliv– 

08:00-09:30 

TuAT6 

Continental 

Ballroom 6 

Symposium: 

Grasping and 

Manipulation: 

Control and 

Learning 

10:00-11:30 

TuBT6 

Continental 

Ballroom 6 

Symposium: 

Grasping and 

Manipulation: 

Mechanics and 

Design 

12:30-13:45 TuPL 

Continental Ballroom 

Plenary I: Design 

14:00-15:30 

TuCT5 

Continental 

Ballroom 5 

Symposium: 


III 

14:00-15:30 

TuCT6 

Continental 

Ballroom 6 

Symposium: 

Grasping and 

Manipulation: 

Grasp Planning 

and Quality 

08:00-09:30 

TuAT7 

Continental 

Parlor 7 

Software 

Architectures & 

Frameworks 

10:00-11:30 

TuBT7 

Continental 

Parlor 7 

Contact and 

Deformation 

14:00-15:30 

TuCT7 

Continental 

Parlor 7 

Mechanisms: 

Actuator Design 

08:00-09:30 

TuAT8 

Continental 

Parlor 8 

Bio-Inspired & 

Biomimetic Robots 

10:00-11:30 

TuBT8 

Continental 

Parlor 8 

Biomimetic 

Limbed Robots 

14:00-15:30 

TuCT8 

Continental 

Parlor 8 

Reptile & Fish- 

Inspired Robots 

08:00-09:30 

TuAT9 

Continental 

Parlor 9 

Probabilistic 

Exploration and 

Coverage 

10:00-11:30 

TuBT9 

Continental 

Parlor 9 

Perceptual 

Learning 

14:00-15:30 

TuCT9 

Continental 

Parlor 9 

Novel Sensors 

08:00-09:30 

TuAPT10 

Golden Gate 

Room 

Interactive II 

10:00-11:30 

TuBPT10 

Golden Gate 

Room 

Interactive III 

14:00-15:30 

TuCPT10 

Golden Gate 

Room 

Interactive IV

IROS 2011 Technical Program at a Glance: Wednesday, September 28, 2011 


08:00-09:30 

WeAT1 

Continental 

Parlor 1 

HRI: Modeling 

Human Behavior 

10:00-11:30 

WeBT1 

Continental 

Parlor 1 

Socially Assistive 

Robots 

14:00-15:30 

WeCT1 

Continental 

Parlor 1 

Human-Robot 

Interaction and 

Cooperation 

16:00-17:30 

WeDT1 

Continental 

Parlor 1 

Human-Robot 

Collaboration 

08:00-09:30 

WeAT2 

Continental 

Parlor 2 

Models and 

Representation 

10:00-11:30 

WeBT2 

Continental 

Parlor 2 

Estimation & 

Sensor Fusion 

14:00-15:30 

WeCT2 

Continental 

Parlor 2 

Pose Estimation 

& Visual Tracking 

16:00-17:30 

WeDT2 

Continental 

Parlor 2 

Recognition & 

Prediction of 

Motion 

08:00-09:30 

WeAT3 

Continental 

Parlor 3 

Medical Robotics: 

Tracking & 

Detection 

10:00-11:30 

WeBT3 

Continental 

Parlor 3 

Surgical Robotics 

14:00-15:30 

WeCT3 

Continental 

Parlor 3 

Robot Safety 

16:00-17:30 

WeDT3 

Continental 

Parlor 3 

Haptic Rendering 

& Object 

Recognition 

08:00-09:30 

WeAT4 

Continental 

Ballroom 4 

08:00-09:30 

WeAT5 

Continental 

Ballroom 5 

Symposium: Symposium: 

Haptics Interfaces Robot Motion 

for the Fingertip, Planning: 

Hand, and Arm Achievements and 

Emerging 

Approaches 

10:00-11:30 

WeBT4 

Continental 

Ballroom 4 

Symposium: 

Hardware and 

Software Design 

for Haptic Systems 

14:00-15:30 

WeCT4 

Continental 

Ballroom 4 

Symposium: 

Haptic Feedback 

and System 

Evaluation 

16:00-17:30 

WeDT4 

Continental 

Ballroom 4 



10:00-11:30 

WeBT5 

Continental 

Ballroom 5 

Symposium: 

Foundations and 

Future Prospects 

of Sampling-Based 

Motion Planning 


–xlv– 

08:00-09:30 

WeAT6 

Continental 

Ballroom 6 

Symposium: 

Aerial Robotics: 

Estimation, 

Perception and 

Control 

10:00-11:30 

WeBT6 

Continental 

Ballroom 6 

Symposium: 

Aerial Robotics: 

Control and 

Planning 

12:30-13:45 WePL 


Plenary II: BioRobotics 

14:00-15:30 

WeCT5 

Continental 

Ballroom 5 

Symposium: 

Symbolic 

Approaches to 


and Control 

16:00-17:30 

WeDT5 

Continental 

Ballroom 5 

Symposium: 

Robot Motion 

Planning: New 

Frameworks and 

High Performance 

14:00-15:30 

WeCT6 

Continental 

Ballroom 6 

Symposium: 

Marine Robotics: 

Platforms and 

Applications 

16:00-17:30 

WeDT6 

Continental 

Ballroom 6 

Symposium: 

Marine Robotics: 

Control and 

Planning 

08:00-09:30 

WeAT7 

Continental 

Parlor 7 

Robot Walking 

10:00-11:30 

WeBT7 

Continental 

Parlor 7 

Passive Walking 

& Leg-Wheeled 

Robots 

14:00-15:30 

WeCT7 

Continental 

Parlor 7 

Humanoid 

Control 

16:00-17:30 

WeDT7 

Continental 

Parlor 7 

Tracking & Gait 

Analysis 

08:00-09:30 

WeAT8 

Continental 

Parlor 8 

Networked Robots 

10:00-11:30 

WeBT8 

Continental 

Parlor 8 

Multirobot 

Systems: 

Rendezvous & 

Task Switching 

14:00-15:30 

WeCT8 

Continental 

Parlor 8 

Multirobot 

Planning 

16:00-17:30 

WeDT8 

Continental 

Parlor 8 

Multirobot 

Coordination & 

Modular Robots 

08:00-09:30 

WeAT9 

Continental 

Parlor 9 

Vision: From 

Features to 

Applications 

10:00-11:30 

WeBT9 

Continental 

Parlor 9 

Visual Servoing 

14:00-15:30 

WeCT9 

Continental 

Parlor 9 

Visual & Multi- 

Sensor Calibration 

16:00-17:30 

WeDT9 

Continental 

Parlor 9 

Calibration & 

Identification 

08:00-09:30 

WeAPT10 

Golden Gate 

Room 

Interactive V 

10:00-11:30 

WeBPT10 

Golden Gate 

Room 

Interactive VI 

14:00-15:30 

WeCPT10 

Golden Gate 

Room 

Interactive VII 

16:00-17:30 

WeDPT10 

Golden Gate 

Room 

Interactive VIII

IROS 2011 Technical Program at a Glance: Thursday, September 29, 2011 


08:00-09:30 

ThAT1 

Continental 

Parlor 1 

Force and 

Stiffness Control 

10:00-11:30 

ThBT1 

Continental 

Parlor 1 

Control for 

Manipulation 

& Grasping 

14:45-16:15 

ThCT1 

Continental 

Parlor 1 

Manipulation 

08:00-09:30 

ThAT2 

Continental 

Parlor 2 

Range and 

RGB-D Sensing 

10:00-11:30 

ThBT2 

Continental 

Parlor 2 

Mapping 

14:45-16:15 

ThCT2 

Continental 

Parlor 2 

Industrial Robots 

16:45-17:45 

ThDT1 

Continental Balllroom 

Forum: 

On Robotics Conferences: 

A Town Hall Meeting 

08:00-09:30 

ThAT3 

Continental 

Parlor 3 

Discrete & 

Kinodynamic 

Planning 

10:00-11:30 

ThBT3 

Continental 

Parlor 3 

08:00-09:30 

ThAT4 

Continental 

Ballroom 4 

08:00-09:30 

ThAT5 

Continental 

Ballroom 5 

Symposium: Symposium: 

Stochasticity in Humanoid 

Robotics and Robotics and 

Biological Systems Biped Locomotion 

I 

Randomized Symposium: 

Planning & Stochasticity in 

Kinematic Control Robotics and 

Biological Systems 

II 

14:45-16:15 

ThCT3 

Continental 

Parlor 3 

Marine Systems 

I 

16:45-17:45 

ThDT3 

Continental 

Parlor 3 

Marine Systems 

II 

10:00-11:30 

ThBT4 

Continental 

Ballroom 4 

14:45-16:15 

ThCT4 

Continental 

Ballroom 4 

Symposium: 

(Self-)assembly 

from the Nano to 

the Macro Scale: 

State of the Art 

and Future 

Directions 

16:45-17:45 

ThDT4 

Continental 

Parlor 2 

Novel System 

Designs: 

Locomotion 

10:00-11:30 

ThBT5 

Continental 

Ballroom 5 

Symposium: 

Humanoid 

Technologies 


–xlvi– 

08:00-09:30 

ThAT6 

Continental 

Ballroom 6 

Symposium: 

Robots that 

Can See I 

10:00-11:30 

ThBT6 

Continental 

Ballroom 6 

Symposium: 

Robots that 

Can See II 

13:30-14:30 ThPT11 


Plenary III: Self-Driving Cars 

14:45-16:15 

ThCT5 

Continental 

Ballroom 5 

Symposium: 

Humanoid 

Applications 

16:45-17:45 

ThDT5 

Continental 

Parlor 1 

Climbing & 

Brachiation 

14:45-16:15 

ThCT6 

Continental 

Ballroom 6 

Symposium: 

Computer Vision 

for Robotics 

16:45-17:45 

ThDT6 

Continental 

Parlor 9 

Novel System 

Designs: Sensing 

and Manipulation 

08:00-09:30 

ThAT7 

Continental 

Parlor 7 

Mechanisms: Joint 

Design 

10:00-11:30 

ThBT7 

Continental 

Parlor 7 

Medical Robots 

& Systems 

14:45-16:15 

ThCT7 

Continental 

Parlor 7 

Exoskeleton 

Robots & Gait 

Rehabilitation 

16:45-17:45 

ThDT7 

Continental 

Parlor 7 

Medical Robotics: 

Motion Planning & 

State Estimation 

08:00-09:30 

ThAT8 

Continental 

Parlor 8 

Autonomous 

Vehicles 

10:00-11:30 

ThBT8 

Continental 

Parlor 8 

Search & Rescue 

Robots 

14:45-16:15 

ThCT8 

Continental 

Parlor 8 

Aerial Robots: 

Navigation, 

Tracking & 

Landing 

16:45-17:45 

ThDT8 

Continental 

Parlor 8 

Aerial Robots 

08:00-09:30 

ThAT9 

Continental 

Parlor 9 

Towards 

Anthropomimetic 

Robots 

10:00-11:30 

ThBT9 

Continental 

Parlor 9 

Intelligent 

Transportation 

Systems 

(Automotive) 

14:45-16:15 

ThCT9 

Continental 

Parlor 9 

Swarms and 

Flocks 

08:00-09:30 

ThAPT10 

Golden Gate 

Room 

Interactive IX 

10:00-11:30 

ThBPT10 

Golden Gate 

Room 

Interactive X 

14:45-16:15 

ThCPT10 

Golden Gate 

Room 

Interactive XI 

16:45-17:45 

ThDPT10 

Golden Gate 

Room 

Interactive XII

14:00-15:30 


Session MoAT1 ⎯⎯ Microsensing 

Session MoAT2 ⎯⎯ Localization 

Session MoAT3 ⎯⎯ Symposium: Robot Audition: Active Audition 

Session MoAT4 ⎯⎯ Symposium: Telerobotics 

Session MoAT5 ⎯⎯ Symposium: Bio-inspired Robotics I 

Session MoAT6 ⎯⎯ Symposium: Field Robotics I 

Session MoAT7 ⎯⎯ Wheeled Robots 

Session MoAT8 ⎯⎯ Learning Parameterized Policies 

Session MoAT9 ⎯⎯ Novel Actuators I 

16:00-17:30 


–1– 

Monday 

September 26, 2011 

Session MoBT1 ⎯⎯ Micromanipulation 

Session MoBT2 ⎯⎯ Localization with Constraints 

Session MoBT3 ⎯⎯ Symposium: Robot Audition: From Sound Source Localization to 

Automatic Speech Recognition 

Session MoBT4 ⎯⎯ Symposium: Robot Demonstrations 

Session MoBT5 ⎯⎯ Symposium: Bio-inspired Robotics II 

Session MoBT6 ⎯⎯ Symposium: Field Robotics II 

Session MoBT7 ⎯⎯ Teleoperation 

Session MoBT8 ⎯⎯ Learning for Control 

Session MoBT9 ⎯⎯ Novel Actuators II


–2– 

Monday 

September 26, 2011

Session MoAT1 Continental Parlor 1 Monday, September 26, 2011, 14:00–15:30 

Microsensing 

Chair Koichi Hashimoto, Tohoku Univ. 

Co-Chair Fumihito Arai, Nagoya Univ. 

14:00–14:15 MoAT1.1 

Evaluation of Biological Clock Activity 

Capsulated by Lipid-mono-layer 

Masaru Kojima 1 , Masahiro Nakajima 2 , Kingo Takiguchi 3 , 

Michio Homma 3 , Takao Kondo 3 and Toshio Fukuda 1,2 

1.Dept. of Micro-Nano Systems Eng., Nagoya Univ., Japan 

2.Center for Micro-Nano Mechatronics, Nagoya Univ., Japan 

3.Div. of biological science, Nagoya Univ., Japan 

• Toward to application, the components of 

the biological clock reconstituted into 

phospholipid-coated microdroplets. 

• The clock activity in phospholipid-coated 

microdroplets was detected as long 

period. 

• Localization of KaiB and KaiC proteins in 

droplets was observed by using 

fluorescent microscopy 

• Different distribution between KaiB and 

KaiC suggest that biased local 

concentration make long period. 

phosphorylation rate 

50 μ m 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

Control 

Droplet 

0.1 

0 

35 hr 35 hr 

0 10 20 30 40 50 60 

70 80 90 100 

time (hr) 

Clock activity in phospholipidcoated 

microdroplets 

14:30–14:45 MoAT1.3 

Temperature Measurement by Color Analysis of 

Fluorescent Spectrum Using Cell Investigation Tool 

Impregnated with Quantum Dot for 

Cell Measurement on a Microfluidic Chip 

Hisataka MARUYAMA, Kyohei TOMITA, Taisuke MASUDA 

Department of Micro-Nano Systems Engineering, Nagoya University, Japan 

Fumihito Arai 


Seoul National University, Korea. 

• Local temperature measurement using 

hydrogel containing temperature sensitive 

quantum dot was achieved. 

• Temperature was measured by color 

information instead of fluorescent intensity 

for long-lifetime temperature 

measurement. 

• Sensitivity of temperature measurement 

was -1.3%/K and accuracy of temperature 

was �0.3 K, respectively. 

• Lifetime of temperature measurement 

using color is much longer than 

temperature measurement using 

fluorescent intensity. 

Fluorescent image of gel-tool 

containing Q-dots 

14:15–14:30 MoAT1.2 

Fast and adaptive auto-focusing algorithm 

for microscopic cell observation 

Takeshi Obara and Koichi Hashimoto 

Graduate School of Information Sciences, Tohoku University, Japan 

Yasunobu Igarashi 

Olympus Software Technology Corp., Japan 

• Auto-focusing algorithm to observe a 

freely motile cell, combining 2 methods 

below 

• Fast auto-focus: Depth from Diffraction 

(DFDi) 

• Adaptive auto-focus: wide scan using 2D 

Laplacian to correct DFDi parameter 

• A freely moving paramecium was autofocused 

and tracked during 45s (see fig.) 


–3– 

Time lapse images of the freely 

moving paramecium 

14:45–14:50 MoAT1.4 

Tracking of Objects in Motion-Distorted Scanning 

Electron Microscope Images 

Christian Dahmen and Sergej Fatikow 

AMiR, University of Oldenburg, Germany 

• Motion of objects induces object 

appearance distortion due to scanning 

nature of scanning electron microscope 

• Adjacent lines have closest temporal 

relation 

• For tracking, a template is decomposited 

into line templates, and the line templates 

flexibly matched 

• This allows tracking of objects where 

standard algorithms may fail due to 

distortions.


Microsensing 

Chair Koichi Hashimoto, Tohoku Univ. 

Co-Chair Fumihito Arai, Nagoya Univ. 

14:50–14:55 MoAT1.5 

Cell Hardness Measurement by Using Twofingered 

Microhand with Micro Force Sensor 

Daiki KAWAKAMI*, Kenichi OHARA*, 

Tomohito TAKUBO*, Yasushi MAE*, Akihiko ICHIKAWA**, 

Tamio TANIKAWA***, Tatsuo ARAI* 

* Graduate School of System Engineering,Osaka Univ., Japan 

** Dept. of Micro-Nano System Engineering, Nagoya Univ.,Japan 

***Intelligent Systems Research Institute, AIST, Japan 

• Two-fingered microhand with micro force 

sensor was developed. 

• The microhand can realize cell operation 

and cell condition measurement. 

• Meyer hardness is focused as a validation 

factor. 

• Calibration strategy obtaining the 

relationships between measured sensor 

data to SI units is shown. 

• The algorithm to obtain Meyer hardness 

by using the microhand is proposed. 

• Validation results are shown through the 

experiments using fibroblast cell 

End-effector with micro force sensor 

Loading and unloading process 

for fibroblast cell 

15:00–15:15 MoAT1.7 

Nanoforce estimation with Kalman filtering 

applied to a force sensor based on diamagnetic 

levitation 

Emmanuel Piat and Joël Abadie and Stéphane Oster 

Femto-st Institute, CNRS – UFC – ENSMM – UTBM, France 

• Nanoforce sensor based on diamagnetic 

levitation 

• Force/displacement transducer based on 

macroscopic seismic mass 

• Deconvolution of displacement using 

Kalman filtering to estimate the force 

• Uncertain a priori modelling of the 

unknown force 

• Trade-off between force resolution and 

sensor response time 

• The size of the image may be adjusted 

14:55–15:00 MoAT1.6 

An ultra-high precision, high bandwidth torque 

sensor for microrobotics applications 

Ben Finio and Robert Wood 

School of Engineering and Applied Sciences, Harvard University, USA 

Kevin Galloway 

Wyss Institute, Harvard University, USA 

• We present, analysis, design, fabrication, calibration and use of a highresolution, 

single-axis torque sensor 

• Sensor fulfills need for torque measurements in micro-Newton-meter 

range, not possible with commercially available sensors 

• As an example application, we measure open-loop pitch torques 

generated by wing motions of a robotic fly 

15:15–15:30 MoAT1.8 

Optimal Design of Non Intuitive Compliant 

Microgripper With High Resolution 

Aymen Grira and Christine Rotinat-Libersa 

CEA –LIST, Interactive Robotics Laboratory France 

Aymen Grira, Bernard Legrand, Estelle Mairiaux, Lionel Buchaillot 

IEMN, Nano and micro systems group, Lille, France 

• New high force-resolution microgripper with 

large jaw displacement, electrostatically 

actuated by a comb-drive and instrumented 

with an integrated differential capacitive 

displacement sensor. 

• This microgripper has been non-intuitively 

designed using a multi-objective optimization 

method, to reach the best compromise 

between chosen performance criteria. 

• It has been modeled and fabricated using 

silicon micro-technology. 

• The theoretical gripping force resolution 

obtained is 78 pN along the direction of the 

mobile tip displacement. 


–4– 

Differential 

capacitive 

sensor 

(a) 

Fixed 

tip 

Mobile 

tip 

Combdrive 

Fixed 

electrodes 

(b) 

actuators 

Sensing 

20 μm 

electrodes 

Mobile 

electrodes 

Fixed 

electrodes 

500 μm 100 μm (c) 

SEM pictures of the fabricated microgripper. 

(a) Overview of the optimized microgripper 

combined with the comb-drive actuator and 

the differential capacitive displacement sensor. 

(b) A closed view of the differential capacitive 

displacement sensor made of 30 fingers. Each 

one is 485 µm long and 4 µm wide. (c) A 

closed view of the electrostatic comb-drive 

actuator made of 68 fingers. Each finger is 20 

µm long and 2 µm wide. The fingers are 

spaced by 2 µm and the overlapping distance 

between fixed and mobile fingers at rest is 10 

µm.


Localization 

Chair Juan D. Tardos, Univ. de Zaragoza 

Co-Chair Antonio Franchi, Max Planck Inst. for Biological Cybernetics 

14:00–14:15 MoAT2.1 

Real-Time Loop Detection 

with Bags of Binary Words 

Dorian Gálvez-López and Juan D. Tardós 

I3A, University of Zaragoza, Spain 

• Real-time loop closure detection applying 

the bag-of-words + geometrical checking 

approach on a binary descriptor space. 

• The feature extraction time is reduced by 

using FAST points with Binary Robust 

Independent Elementary Features 

(BRIEF). 

• Geometrical checking is speeded up by 

pre-filtering correspondence points with a 

direct index. 

• Extracting features, detecting loops in a 

database with 19K images and performing 

geometrical checking take less than 50 

milliseconds. 

Execution time with 2723 images. 

Precision: 100%, Recall: 57.86% 

14:30–14:45 MoAT2.3 

An Observability-Constrained 

Sliding Window Filter for SLAM 

Guoquan Huang, and Stergios Roumeliotis 

Dept. of Computer Science and Engineering, Univ. of Minnesota, USA 

Anastasios Mourikis 

Dept. of Electrical Engineering, Univ. of California, Riverside, USA 

• Sliding-Window Filter (SWF): 

A resource-adaptive smoother, 

better than filtering at dealing with 

nonlinearity 

• Inconsistency of standard SWF: 

rank of the Hessian is erroneously 

increased 

• Observability-Constrained (OC)- 

SWF selects linearization points to 

ensure correct rank of Hessian 

while minimizing linearization errors 

OC-SWF: better consistency and 

accuracy than competing approaches, 

performs closer to batch Maximum-A- 

Posterior (MAP) estimator in tests 

14:15–14:30 MoAT2.2 

Best-First Branch and Bound Search Method for 

Map Based Localization 

Jari Saarinen, Janne Paanajärvi and Pekka Forsman 

Automation and Systems technology, Aalto University, Finland 

• An efficient algorithm that calculates 

globally optimal pose estimate 

• The algorithm is based on best-first 

branch and bound search in the 

relative pose space between the data 

sets 

• Provides also an estimation of the 

accuracy 


–5– 

The measurement is evaluated in 

the center of the cell, for which 

the score and upper bound 

are estimated 

14:45–14:50 MoAT2.4 

Corrective Gradient Refinement for 

Mobile Robot Localization 

Joydeep Biswas, Brian Coltin, Manuela Veloso 

School of Computer Science, Carnegie Mellon University, USA 

• Corrective Gradient Refinement uses 

state space derivatives of observation 

likelihood to refine proposal distributions 

• Efficient, Analytic computation using 

Vector Maps and 2D (Laser rangefinder) 

and 3D (Kinect Depth Camera) point cloud 

observation models 

• Experimentally shown to be more 

accurate, and require far fewer particles 

than Sampling / Importance Resampling 

Monte Carlo Localization 

• Long term trials (> 13km traversed indoors 

over 19 days) demonstrate reliability 

CGR 

SIR - MCL 

CGR efficiently samples 

directions of uncertainty based on 

observations


Localization 

Chair Juan D. Tardos, Univ. de Zaragoza 

Co-Chair Antonio Franchi, Max Planck Inst. for Biological Cybernetics 

14:50–14:55 MoAT2.5 

A Monocular Vision-based System 

for 6D Relative Robot Localization 

Andreas Breitenmoser, Laurent Kneip and Roland Siegwart 

Autonomous Systems Laboratory, ETH Zurich, Switzerland 

• Novel relative localization system with 

two complementary modules: 

• Camera module: monocular vision 

• Target module: 4 active or passive 

markers 

• 6D relative localization in 3D space, 

based on target design, P3P-localization, 

blob extraction & pose prediction 

• Experimental validation on a quadrotor 

helicopter and on a team of e-puck robots 

• Accuracies of a few centimeters in 

position & a few degrees in orientation 

Relative localization modules 

installed on three e-puck robots 

15:00–15:15 MoAT2.7 

Loop-closure candidates selection by exploiting 

structure in vehicle trajectory 

J. Nieto, G. Agamennoni and T. Vidal-Calleja 

Australian Centre for Field Robotics, The University of Sydney, Australia 

• Detecting loops is a key problem in robot 

localisation. 

• Most of the current techniques use 

observations of the environment as main 

features to produce loop-hypotheses. 

• In this paper we investigate the feasibility 

of producing loop candidates from 

features of the robot trajectory. 

• We present an alignment likelihood 

function to measure similarity between 

trajectory sequences. 

• Experimental results with data collected in 

Sydney CBD are used to validate our 

approach. 

Estimated trajectory with loop 

candidates selected 

14:55–15:00 MoAT2.6 

Accurate Human Motion Capture in Large Areas 

by Combining IMU- and Laser-Based 

People Tracking 

Jakob Ziegler, Henrik Kretzschmar, 

Cyrill Stachniss, and Wolfram Burgard 

Dept. of Computer Science, University of Freiburg, Germany 

Giorgio Grisetti 

Dept. of Systems and Computer Science, Sapienza University of Rome, Italy 

• Estimate the globally aligned full body 

posture of a person 

• Combine an inertial motion capture suit 

with laser-based people tracking 

• Formulate a joint least squares 

optimization problem to compute a 

maximum likelihood trajectory estimate 

given all measurements 

• Experimental evaluation in large outdoor 

scenes 

15:15–15:30 MoAT2.8 

Optimized Motion Strategies for Localization in 

Leader-Follower Formations 

Xun S. Zhou*, Ke X. Zhou†, and Stergios I. Roumelitotis* 

*Dept. of Computer Science & Engineering, Univ. of Minnesota, USA 

†Dept. of Eletrical & Computer Engineering, Univ. of Minnesota, USA 

• Main issue: Robot-to-robot pose is 

unobservable using distance- or bearing-only 

measurements, when moving in formation. 

• Key idea: Allow follower to deviate from the 

desired formation within a predefined region 

to minimize the localization uncertainty. 

• Formulated as non-convex optimization 

problems 

• Key contribution: 

(a) Analyze the trade-off between the 

formation and the estimation accuracy; 

(b) Analytically compute the global optimal 

motion strategy (by solving a system of 

multivariate polynomials). 

• Performance: Indistinguishable from gridbased 

exhaustive search, and significantly 

better than state of the art. 


–6– 

{F} 

{L} 

The follower (F) seeks the next 

best pose within a disk centered 

at the desired position p o with 

radius r, such that the localization 

uncertainty is minimized.


Symposium: Robot Audition: Active Audition 

Chair Patrick Danès, Univ. de Toulouse ; LAAS-CNRS ; UPS ; F-31077 

Co-Chair Ian Lane, Carnegie Mellon Univ. Silicon Valley 

14:00–14:15 MoAT3.1* 

Semi-Plenary Invited Talk: An Overview of the 

BINAAHR Project 

Patrick Danès, Université de Toulouse ; LAAS-CNRS ; UPS ; 

F-31077 

Hiroshi G. Okuno, Kyoto University 

14:30–14:45 MoAT3.3 

Active Soft Pinnae for Robots 

Makoto Kumon and Yoshitaka Noda 

Graduate School of Science and Technology, Kumamoto University, Japan 

• A novel pinna system that can deform its 

shape actively is proposed. 

• The pinna has a soft deformable skin 

made of silicone rubber, and it is covered 

with fur. 

• Kinematic model of the pinna is 

investigated, and it is validated by 

experiments. 

• Measured acoustic characteristics shows 

that the pinna can focus the sound coming 

from the front, and that it can encode the 

elevation of the sound source as a 

frequency cue. 

14:15–14:30 MoAT3.2 

Assessment of Single-channel 

Ego Noise Estimation Methods 

Gökhan Ince 1,2 , Kazuhiro Nakadai 1,2 , Tobias Rodemann 3 , 

Jun-ichi Imura 2 , Keisuke Nakamura 1 and Hirofumi Nakajima 1 

1- Honda Research Institute Japan Co., Ltd. 2- Tokyo Institute of Tech., Japan 

3- Honda Research Institute Europe GmbH 

• Problem: Ego noise (Fan noise + motor 

noise generated during robot motions) 

• Method: Instantaneous estimation of ego 

noise using parameterized templates 

• Evaluation: Performance comparison 

with other state-of-the-art single-channel 

noise estimation methods 

• Results: 

1) Reduced estimation error 

2) Reduced log spectral distortion 

3) Improved signal-to-noise ratio 

4) Improved word correct rates 

during the arm and head motion of a 

humanoid robot in the presence of 

changing background noise 


–7– 

Noisy signal 

Enhanced signal 

Yes sir, 

we are cool! 

Robots 

are cool! 

14:45–14:50 MoAT3.4 

Particle-filter Based Audio-visual Beat-tracking 

for Music Robot Ensemble with Human Guitarist 

Tatsuhiko Itohara, Takuma Otsuka ,Takeshi Mizumoto 

Tetsuya Ogata, and Hiroshi G. Okuno 

Graduate School of Informatics, Kyoto University, Japan 

• Beat-tracking, estimation of tempo 

and beat-times, is critical to the high 

quality of musical ensemble. 

• Most conventional methods lack the 

adaptation of some feature of guitar 

performances, such as back-beats. 

• Our method integrates the audio and 

visual information with a particle filter 

method. 

• Experimental results reveal the 

robustness against the guitar features 

and real-time performance. 

A ensemble by a thereminist 

robot and a human guitarist


Symposium: Robot Audition: Active Audition 

Chair Patrick Danès, Univ. de Toulouse ; LAAS-CNRS ; UPS ; F-31077 

Co-Chair Ian Lane, Carnegie Mellon Univ. Silicon Valley 

14:50–14:55 MoAT3.5 

Optimizing a Reconfigurable Robotic 

Microphone Array 

Eric Martinson, Thomas Apker, Magda Bugajska 

Navy Center for Applied Research in Artificial Intelligence 

Naval Research Laboratory, USA 

Omnidirectional 

Camera 

Microphones, 

with Fiducials 

• A distributed, robotic microphone array is deployed to localize sound 

sources in predetermined regions. 

• The array shape, (relative microphone locations), impact sound 

localization accuracy. 

• Random array shapes often have poor performance. 

• Optimizing robotic positions for the region of interest improves sound 

localization accuracy > 70%. 

15:00–15:15 MoAT3.7 

Acoustic Models and Kalman Filtering Strategies 

for Active Binaural Sound Localization 

Alban Portello † , Patrick Danès † , Sylvain Argentieri ‡ 

† CNRS-LAAS, Université de Toulouse, France 

‡ ISIR-CNRS, UPMC, France 

• A generic framework to the localization of 

a moving sound source from a moving 

binaural sensor is presented. 

• An accurate model of binaural cues in the 

presence of motion is set up. 

• A multi-hypothesis estimation scheme is 

proposed, which can ensure a correct 

(non-overconfident) localization with no 

prior knowledge. 

• The strategy is shown to resolve frontback 

ambiguities and to provide range 

information about the source. 

• The approach is validated on simulated 

and experimental results. 

(a) Sensor motion and and emitter 

position estimate in the world 

frame f (b) Range R and d azimuth i th 

estimates in the sensor frame along 

time 

14:55–15:00 MoAT3.6 

Incremental Learning for 

Ego Noise Estimation of a Robot 

Gökhan Ince 1,2 , Kazuhiro Nakadai 1,2 , Tobias Rodemann 3 , 

Jun-ichi Imura 2 , Keisuke Nakamura 1 and Hirofumi Nakajima 1 

1- Honda Research Institute Japan Co., Ltd. 2- Tokyo Institute of Tech., Japan 

3- Honda Research Institute Europe GmbH 

• Problem: Template estimation suffers 

from constantly growing database size 

• Method: Incremental learning of the 

templates with an update mechanism 

• Results: 

1) Reduced database size 

2) Reduced log spectral distortion 

3) Reduced noise estimation error 

4) Improved signal-to-noise ratio 

5) Improved word correct rates 

during the arm and head motion of a 

humanoid robot in the presence of 

changing background noise 


–8– 

Number of templates in the 

database 

2000 

Standard template 

1800 

learning 

1600 

Incremental learning 

1400 of templates 

1200 

1000 

800 

600 

400 

200 

0 

1 2 3 4 5 

Number of motion repetitions 

Yes sir, we are 

totally cool! 

Robots are 

cool! 

15:15–15:30 MoAT3.8 

Intelligent Sound Source Localization and Its 

Application to Multimodal Human Tracking 

Keisuke Nakamura, Kazuhiro Nakadai 

Futoshi Asano, and Gökhan Ince 

Honda Research Institute Japan Co., Ltd., Japan 

• The proposed human tracking system in a 

real environment by intelligent sound 

source localization (SSL) achieves: 

1. Intelligent sound source tracking based 

on the sort of sound source such as 

speech, music, and so on by integrating 

SSL with sound source identification 

2. Highly-robust human tracking by 

multimodal integration of a microphone 

array, ToF camera, and thermo camera 

even when sensor information is 

missing or ambiguous due to occlusion 

or acoustic noise. 

Intelligent Human 

Tracking System

Session MoAT4 Continental Ballroom 4 Monday, September 26, 2011, 14:00–15:30 

Symposium: Telerobotics 

Chair Angelika Peer, Tech. Univ. München 

Co-Chair M. Cenk Cavusoglu, Case Western Res. Univ. 

14:00–14:15 MoAT4.1* 

Semi-Plenary Invited Talk: Development of VGO 

Robotic Telepresence System 

Thomas Ryden, VGO Communications 

14:30–14:45 MoAT4.3 

Hybrid Virtual-Proxy Based Control Framework 

for Passive Bilateral Teleoperation over Internet 

Ke Huang* and Dongjun Lee + 

* MABE Department, University of Tennessee - Knoxville, USA 

+ School of MAE, Seoul National University, South Korea 

• Novel hybrid control framework for bilateral 

teleoperation over packet-switched 

network with varying-delay, packet loss, 

data swapping, etc. 

• Key innovation is to exploit oftenoverlooked 

hybrid nature of teleoperation 

(i.e., continuous robot, discrete comm., 

and sampled-date control) 

• Closed-loop passivity guaranteed by a 

combination of discrete/adjustable virtual 

proxy (VP) damping and continuous/unadjustable 

device damping 

• Large device damping requirement of VPless 

direct PD-coupling (IFAC11) for 

severe comm. imperfectness is removed 

System structure of VP based 

bilateral teleoperation 

14:15–14:30 MoAT4.2 

Semi-Plenary Invited Talk: Development of VGO 

Robotic Telepresence System 

Thomas Ryden, VGO Communications 

14:45–14:50 MoAT4.4 

Mutual Telexistence Surrogate System: TELESAR4 

- telexistence in real environments using 

autostereoscopic immersive display - 

Susumu Tachi, Kouichi Watanabe, Keisuke Takeshita, 

Kouta Minamizawa, Takumi Yoshida, and Katsunari Sato 

Graduate school of Media Design, Keio University, Japan 

• TELESAR4 affords a remote person the 

opportunity to virtually participate in some 

event, such as a party or a meeting. 

• The system provides the remote 

participant with a life size 360˚ wide-range 

stereo view of the event venue with local 

participants in real time. 

• The local participants at the event are able 

to see the face and expressions of the 

remote participant in real time. 

• The system also allows the remote 

participant to move freely about the venue 

and to perform some manipulatory tasks 

such as handshake and gestures. 


–9– 

a 

b 

(a) Remote participant virtually 

attending an event. 

(b) Local participants and 

surrogate robot in the venue.


Symposium: Telerobotics 

Chair Angelika Peer, Tech. Univ. München 

Co-Chair M. Cenk Cavusoglu, Case Western Res. Univ. 

14:50–14:55 MoAT4.5 

Experiments of Passivity-Based Bilateral Aerial 

Teleoperation of a Group of UAVs with 

Decentralized Velocity Synchronization 

Paolo Robuffo Giordano * , Antonio Franchi * , Cristian Secchi ** , and 

Heinrich H. Bülthoff * *** 

* Max Planck Institute for Biological Cybernetics, Germany 

** DISMI, University of Modena and Reggio Emilia, Italy 

*** DBCE, Korea University, Korea 

• Decentralized Bilateral Teleoperation 

of a group of UAVs 

• Time-varying topology (arbitrary 

split/join) is handled in a stable (passive) 

way 

• Formal proof of steady-state velocity 

synchronization via a variable damping 

action 

• Experiments with 4 Quadcopters (slaveside) 

and a Omega.3 force-feedback 

device (master-side) 

Experiments of 4 Quadcopters 

teleoperated in a cluttered 

environment 

15:00–15:15 MoAT4.7 

Network Representation and Passivity of 

Delayed Teleoperation Systems 

J.Artigas, C. Preusche, G. Hirzinger 

Institute of Robotics and Mechatronic, German Aerospace Center, Germany 

J-H. Ryu 

Biorobotics Laboratory, Korean University of Technology, South Korea 

• General network based 

representation, analysis and 

design. 

• A solution to the ambiguity of the 

channel causality (with lack of effort 

or flow transmitted!). 

• A framework to design 

teleoperation architectures: 

• Time delay independency 

• For any channel architecture 

• Stability through Time Domain 

Passivity. 

14:55–15:00 MoAT4.6 

Design of Single-Operator-Multi-Robot 

Teleoperation Systems with 

Random Communication Delays 

Yunyi Jia, Ning Xi and John Buether 

Electritical and Computer Engineering Department, 

Michigan State University, USA 

• Propose a system structure for 

designing Single-Operator-Multi- 

Robot Teleoperation Systems 

• Non-time based control is applied 

to cope with random 

communication delays 

• Convenient to change the multirobot 

structure, like adding or 

removing robots 


–10– 

Single-Operator-Multi-Robot 

Teleoperation System 

15:15–15:30 MoAT4.8 

Controlling Telerobotic Operations Adaptive to 

Quality of Teleoperator and Task Dexterity 

Yunyi Jia, Ning Xi, Fei Wang, Yunxia Wang and Xin Li 

Electritical and Computer Engineering Department, 

Michigan State University, USA 

• Propose a concept named Quality 

of Teleoperator (QoT) 

• Integrate the quality of 

teleoperator into teleoperation 

system 

• Adjust controller parameters 

adaptive to QoT and Task 

dexterity 

• Teleoperation efficiency and 

safety are enhanced 

Telerobotic systems adaptive to QoT and TDI


Symposium: Bio-inspired Robotics I 

Chair Justin Seipel, Purdue 

Co-Chair Xinyan Deng, Purdue Univ. 

14:00–14:15 MoAT5.1* 

Semi-Plenary Invited Talk: Robustness in Animals as 

Inspiration for the Next Generation Robot 

Robert Full, University of California at Berkeley 

14:30–14:45 MoAT5.3 

A MATLAB Framework for Efficient Gait Creation 

C. David Remy and Roland Siegwart, Fellow, IEEE 

Autonomous Systems Lab, ETH Zurich, Switzerland 

Keith Buffinton 

Dept. of Mech Engr., Bucknell University, Lewisburg PA, USA 

• The paper introduces a framework for the 

creation of efficient gaits that can exploit 

natural dynamics. 

• The presented tools and methods are 

illustrated with the following three 

examples: 

• The stability analysis of a passive 

dynamic walker 

• An analysis of the efficiency (COT) of 

an actuated monopod hopper 

• The control of a bounding quadruped 

14:15–14:30 MoAT5.2* 

Semi-Plenary Invited Talk: Snake-inspired Robots 

Shigeo Hirose, Tokyo Institute of Technology 

14:45–14:50 MoAT5.4 

A Controller for Continuous Wave 

Peristaltic Locomotion 

Alexander S. Boxerbaum, Andrew D. Horchler, Roger D. Quinn 

Mechanical Engineering, Case Western Reserve University, USA 

Kendrick M. Shaw, Hillel J. Chiel 

Biology, Case Western Reserve University, USA 

• Recent successes with a continuously deformable wormlike robot have 

informed the investigation of novel soft-bodied control strategies 

• A dynamic analysis of peristalsis suggests ways to minimize slippage 

• A Wilson-Cowan model of 

neuronal populations is explored 

as a means to generate many 

different waveforms 

• Unlike CPG networks, this 

model can readily change its 

spatial and temporal 

frequencies, even coming to a 

complete stop 


–11– 

A wormlike robot with a continuously 

deformable outer mesh


Symposium: Bio-inspired Robotics I 

Chair Justin Seipel, Purdue 

Co-Chair Xinyan Deng, Purdue Univ. 

14:50–14:55 MoAT5.5 

Energetics of Bio-Inspired Legged Robot 

Locomotion with Elastically-Suspended Loads 

Jeffrey Ackerman and Justin Seipel 

School of Mechanical Engineering, Purdue University, USA 

• Loads such as batteries, electronics, and 

fuel can be elastically-suspended from a 

robot 

• Elastically-suspended loads increase the 

energy efficiency of legged locomotion 

• Thus, a robot’s speed, operating time, or 

load carrying capacity could be increased 

Prototype robot with an 

elastically-suspended load 

15:00–15:15 MoAT5.7 

Descending Commands to an Insect Leg Controller 

Network Cause Smooth Behavioral Transitions 

Brandon Rutter, Brian Taylor, John Bender, Roy Ritzmann and 

Roger Quinn 

Mech. & Aero Eng., and Biology, Case Western Reserve Univ., USA 

William Lewinger 

Inst. Of Perception, Action & Behavior, U. of Edinburgh, UK 

Marcus Blümel 

Dept. of Animal Physiology, Inst. For Zoology, U. of Cologne, Germany 

• Leg Controller Network (LegConNet) 

controls forward stepping of a robotic 

model cockroach leg. 

• Hypothesized additional pathways allow 

the model to smoothly transition to insectlike 

turning behaviors. 

• The modification of these pathways and 

their strengths is equivalent to descending 

commands in the animal 

• Several experiments were run showing 

the operation of the system under differing 

timing of descending commands. 

14:55–15:00 MoAT5.6 

A Rapidly Reconfigurable Robot for Assistance 

in Urban Search and Rescue 

Alexander Hunt, Richard Bachmann and Roger Quinn 

Department of Mechanical and Aerospace Engineering 

Case Western Reserve University, USA 

Robin Murphy 

Department of Computer Science and Engineering, Texas A&M, USA 

• USAR Whegs TM successfully implements 

features desirable for search and rescue 

on a mobile robot 

• Relatively lightweight and compact 

• Four wheel-legs and differential steering 

• Carbon fiber wheel-legs for strength and 

reduction of rotary inertia by factor of eight 

• Quick change devices allow the robot to 

be changed to/from tracks and wheel-legs 

• Geosystems Zippermast for elevating 

camera field of view up to 8 feet (xx 

meters) 


–12– 

Robot with mast raised next to a 

74 inch (188 cm) tall man 

15:15–15:30 MoAT5.8 

Virtual Chassis for Snake Robots 

David Rollinson and Howie Choset 

The Robotics Institute, Carnegie Mellon University, USA 

• A new method for determining 

intuitive body coordinate frames for 

snake robots. 

• Separates internal motion due to the 

robot’s shape changes from external 

motion in the world. 

• A general method is presented for 

any shape configuration. 

• Additional refinements can be made 

in certain cases that exploit the 

symmetry of the robot’s shape.


Symposium: Field Robotics I 

Chair Satoshi Tadokoro, Tohoku Univ. 

Co-Chair David A. Anisi, ABB 

14:00–14:15 MoAT6.1* 

Semi-Plenary Invited Talk: Rescue mobile robot 

Quince --Toward emergency response to the nuclear 

accident at Fukushima Daiichi Nuclear Power Plant on 

March 2011 

Keiji Nagatani, Tohoku University 

Tomoaki Yoshida, Chiba Institute of Technology 

14:30–14:45 MoAT6.3 

Perception for a River Mapping Robot 

Andrew Chambers, Supreeth Achar, Stephen Nuske, Jörn Rehder, 

Lyle Chamberlain, Justin Haines, Sebastian Scherer, Sanjiv Singh 

Robotics Institute, Carnegie Mellon University, USA 

Bernd Kitt 

The Institute of Measurement and Control Systems, 

Karlsruhe Institute of Technology, Germany 

• Perception system for mapping rivers with 

thick canopy 

• 360� obstacle detection with off-axis 

spinning laser 

• Adaptive appearance model for river 

detection and map generation 

• Graph-based global state estimation from 

visual odometry, inertial measurements, 

and intermittent GPS 

3D Point Cloud overlaid on 2km 

river traverse 

14:15–14:30 MoAT6.2 

Semi-Plenary Invited Talk: Rescue mobile robot 

Quince --Toward emergency response to the nuclear 

accident at Fukushima Daiichi Nuclear Power Plant on 

March 2011 

Keiji Nagatani, Tohoku University 

Tomoaki Yoshida, Chiba Institute of Technology 

14:45–14:50 MoAT6.4 

Real-world demonstration of sensor-based 

robotic automation in oil & gas facilities 

David A. Anisi, Erik Persson and Clint Heyer 

Department for Strategic R&D for Oil, Gas 

and Petrochemicals, ABB, Norway 

• On-site demonstration of autonomous 

valve manipulation and thermal inspection 

• Both operations were requested by the 

current site operators 

• Run amidst live hydrocarbon processes 

• The first system performing sensor-based, 

close-contact operations in a real 

operational environment (ATEX) 

• Using torque overload detection method to 

avoid over-tightening/loosening the valve 

• Rely on four independent layers of safety 

to meet the level of robustness, accuracy 

and reliability required by the industry 


–13– 

Industrial robot certified for running 

in explosive atmospheres (ATEX)


Symposium: Field Robotics I 

Chair Satoshi Tadokoro, Tohoku Univ. 

Co-Chair David A. Anisi, ABB 

14:50–14:55 MoAT6.5 

Offshore Robotics – 

Survey, Implementation, Outlook 

Kai Pfeiffer, Alexander Bubeck and Matthias Bengel 

Robot Systems Department, Fraunhofer Institute for 

Manufacturing Engineering and Automation (IPA), Germany 

• Survey of production operation tasks on 

offshore installations shows potential for 

robotic automation 

• MIMROex: World wide first ATEX 

certifiable mobile inspection robot tested 

on an offshore installation 

• Extension of robot control system for 

assisted tele-manipulation for 

interaction with process equipment 

Mobile inspection robot MIMROex 

on an offshore installation in the 

south Chinese sea 

15:00–15:15 MoAT6.7 

Combining Radar and Vision for Self-Supervised 

Ground Segmentation in Outdoor Environments 

Annalisa Milella 

Institute of Intelligent Systems for Automation, CNR, Italy 

Giulio Reina 

Department of Engineering for Innovation, University of Salento, Italy 

James Underwood, and Bertrand Douillard 

Australian Centre for Field Robotics, University of Sydney, Australia 

• Self-supervised radar-vision classification 

system that allows an autonomous vehicle 

to automatically construct online a visual 

model of the ground and perform accurate 

ground segmentation. 

• Automatic online labeling based on a 

radar ground segmentation approach prior 

to image analysis to avoid time consuming 

manual labeling to construct the training 

set. 

• Adaptive ground model learning by 

retraining the classifier using the most 

recent radar scans. 

14:55–15:00 MoAT6.6 

Active camera control with obstacle avoidance for 

remote operations with industrial manipulators: 

Implementation and experimental results 

Magnus Bjerkeng and Kristin Pettersen 

Department of Engineering Cybernetics, NTNU, Norway 

Aksel Transeth, Erik Kyrkebø and Sigurd Fjerdingen 

SINTEF ICT Applied Cybernetics, Norway 

• A controller for dynamic surveillance of 

remote operations for enhanced 

remote awareness is presented. 

• A pseudoinverse controller based on 

the stereographic projection achieves 

real-time camera tracking and tunable 

camera distance. 

• Obstacle avoidance is achieved using 

artificial potential fields respecting joint 

limitations without oscillations. 

• Experiments using industrial 

manipulators are presented. 

15:15–15:30 MoAT6.8 

Assessing the Deepwater Horizon Oil Spill with 

the Sentry Autonomous Underwater Vehicle 

James C. Kinsey 1 , Dana R. Yoerger 1 , Michael V. Jakuba 2 , 

Rich Camilli 1 , Charles R. Fisher 3 , and Christopher R. German 1 

1 Woods Hole Oceanographic Institution, USA 

2 Australian Centre for Field Robotics, Australia 

3 Pennsylvania State University, USA 

• The Sentry AUV is an autonomous 

underwater robot capable of operations to 

4500m 

• In 2010, Sentry was deployed on two 

cruises in response to the Deepwater 

Horizon Oil Spill 

• In June 2010, Sentry was equipped with 

the TETHYS mass spectrometer [Camilli 

and Duryea, 2009] and tracked an 

underwater oil plume up to 35km from the 

spill site 

• Another cruise in December 2010 did 

bathymetric and photo surveys to assess 

the impact of the spill on coral 

communities 


–14– 

Top: Sentry AUV at the DWH Oil Spill. 

Bottom: Methane and water velocity 

measurements of the plume 

[Camilli et al., 2010]



Chair Yasuhisa Hirata, Tohoku Univ. 

Co-Chair Matthew Spenko, Illinois Inst. of Tech. 

14:00–14:15 MoAT7.1 

SO(2) and SO(3), Omni-Directional Personal Mobility 

with Link-Driven Spherical Wheels 

Sungsuk Ok, Atsushi Kodama, Yuma Matsumura 

and Yoshihiko Nakamura 

Mechano-Informatics, The University of Tokyo, Japan 

• This paper proposed Link-Driven 

Spherical Wheel composed of three serial 

links jointed to the spherical wheel. 

• It is realized that LDSW move to any 

direction by designed controller. 

• The SO(3) installing 3LDSW is developed 

for a hospital bed or a personal mobility 

and realized to move to omni-direction by 

designed controller. 

• The SO(2) installing 2LDSW is developed 

for a personal mobility and experimented 

by designed controller based on the 

dynamics and kinematics of mobility. 

The overview of Link-Driven 

Spherical Wheel & Mobilities 

( LDSW, SO(2) and SO(3) ) 

14:30–14:45 MoAT7.3 

Wheel-Soil Interaction Model for Rover 

Simulation Based on Plasticity Theory 

Ali Azimi, Jozsef Kovecses, and Jorge Angeles 

Department of Mechanical Engineering 

and Centre for Intelligent Machines 

McGill University, Canada 

• A novel approach based on plasticity 

theory is introduced here, which is 

computationally efficient 

• The simulation results are verified with 

experimentally validated semiempirical 

models in steady-state 

operations 

• This approach can address some of 

the shortcomings of the semi-empirical 

models, like slip-sinkage phenomenon 

• Our approach is well suited for 

implementation in a multibody 

dynamics simulation environment like 

Vortex 

Sojourner Simulation in Vortex 

14:15–14:30 MoAT7.2 

Design and control of an active anti-roll system 

for a fast rover 

Mohamed Krid and Faiz Ben Amar 

Institut des Systèmes Intelligents et de Robotiques 

Université Pierre et Marie Curie UPMC Paris 6, CNRS UMR7222, France 

• Fast off-road rover with high clearance 

and large suspension displacement 

• Design of an innovative anti-roll active 

device and its kinematic modeling 

• Roll dynamics modeling 

• Stability metrics defined by Lateral Load 

Transfer 

• Design of a model predictive control MPC 

for roll control and optimal stability 

considering the path following control 


–15– 

Figure caption is optional, 

use Arial Narrow 20pt 

14:45–14:50 MoAT7.4 

Using Unmanned Ground Vehicle Performance 

Measurements as a Unique Method of Terrain 

Classification 

Siddharth Odedra 

Autonomous Systems Lab, Middlesex University, UK 

• An Unmanned Ground Vehicles’ biggest 

obstacle is terrain; which can be 

unpredictable, unstructured and 

untraversable. 

• Systems with more knowledge about 

terrain will have greater success when 

operating in unforgiving conditions. 

• It is proposed that terrain can be classified 

by measuring its effect on vehicle 

performance. 

Vehicle platform used to 

measure the vehicle-terrain 

interaction.



Chair Yasuhisa Hirata, Tohoku Univ. 

Co-Chair Matthew Spenko, Illinois Inst. of Tech. 

14:50–14:55 MoAT7.5 

A Multi-tiered Robust Steering Controller Based 

on Yaw Rate and Side Slip Estimation 

Ming Xin and Mark Minor 

Mechanical Engineering Department, University of Utah, USA 

• Model considers curvature based vehicle 

kinematics, and slip angle and yaw rate 

dynamics. 

• Multi-tiered sliding mode control well 

matched to vehicle sensors, actuators, 

and dynamics. 

• High gain observer estimates slip angle 

and yaw rate for output feedback control 

• Simulations and experiment verify 

performance at high and low speeds 

The Experimental Platform – 

“Red Rover” 

15:00–15:15 MoAT7.7 

A Diameter-Dependent Terramechanics Model 

for Small-Wheeled UGVs on Deformable Terrain 

Gareth Meirion-Griffith and Matthew Spenko 

Mechanical, Materials and Aerospace Engineering Department, Illinois Institute 

of Technology, U.S.A. 

• In this paper a diameter-dependent 

pressure-sinkage model is introduced. 

• This model is used to derive a modified 

terramechanics framework for smallwheeled 

UGVs. 

• This framework is used to simulate vehicle 

performance on deformable terrains. 

• Field tests using an experimental UGV are 

used to validate this model. 

Elliptical distribution of normal 

stress at the wheel-soil interface 

14:55–15:00 MoAT7.6 

Differential flatness of a front-steered vehicle 

with tire-force control 

Steven Peters and Karl Iagnemma 

Dept. of Mechanical Engineering, MIT, USA 

Emilio Frazzoli 

Lab for Information and Decision Systems (LIDS), MIT, USA 

• The position of the front center of 

oscillation of a bicycle is a flat output 

• Applying tire force control allows this 

point to track desired trajectories, 

even at slip angles of 10-20 degrees 

• Structure in yaw dynamics resembling 

Liénard system enables nonlinear 

stability analysis of 3 rd degree of 

freedom, yaw dynamics 

15:15–15:30 MoAT7.8 

Development of Passive type Double 

Wheel Caster Unit based on Analysis of 

Feasible Braking Force and Moment Set 

Masao Saida, Yasuhisa Hirata and Kazuhiro Kosuge 

Engineering, Tohoku University, Japan 

• We introduce passive mobile robots 

consisting of various wheels which are 

controlled by servo brakes. 

• We analyze a feasible braking 

force/moment necessary for controlling 

the robot with servo brakes. 

• We reveal the advantages and 

disadvantages of the feasible braking 

force/moment of each robot. 

• We propose a new passive type double 

wheel caster unit, called PDC. 


–16– 

F yr 

x r 

Izz mxr c 

Fxf 

Fyf 

Feasible Braking Force and Moment 

Passive type Double Wheel Caster Unit 

δ


Learning Parameterized Policies 

Chair Jan Peters, Darmstadt Univ. of Tech. 

Co-Chair Petar Kormushev, Istituto Italiano di Tecnologia 

14:00–14:15 MoAT8.1 

Bipedal Walking Energy Minimization 

by Reinforcement Learning with 

Evolving Policy Parameterization 

Petar Kormushev, S. Calinon, N. G. Tsagarakis, D. G. Caldwell 

Advanced Robotics, Italian Institute of Technology (IIT), Italy 

Barkan Ugurlu 

Toyota Technological Institute (TTI), Japan 

• New reinforcement learning approach 

evolves the policy parameterization 

dynamically during the learning process 

• Advantages: 

• Faster convergence 

• Avoids local optima 

• Decreased computation time 

• Applications: 

• Function approximation task 

• Bipedal robot walking energy 

consumption minimization 

• Variable-CoM-height ZMP-based walk 

generator uses efficiently the passive 

compliance of the robot 

Passively-compliant robot COMAN 

walking with varying CoM-height 

14:30–14:45 MoAT8.3 

Learning Anticipation Policies 

for Robot Table Tennis 

Zhikun Wang 1,2 , Christoph H. Lampert 3 , Katharina Mülling 1,2 , 

Bernhard Schölkopf 1 , Jan Peters 1,2 

1 MPI for Intelligent Systems 2 TU Darmstadt 3 IST Austria 

• The robot learns to play table tennis against human players. 

• We develop an anticipation system that allows the robot to 

• perceive the opponent’s moves; 

• predict his intention; 

• react accordingly with optimized hitting plans. 

• The anticipation policies are learned using reinforcement learning. 

• Figure. The optimal hitting plan is chosen by the learned anticipation policies. 

14:15–14:30 MoAT8.2 

Learning Motion Primitive Goals 

For Robust Manipulation 

Freek Stulp, Evangelos Theodorou, Mrinal Kalakrishnan, 

Peter Pastor, Ludovic Righetti, Stefan Schaal 

Computational Learning and Motor Control Lab, 

University of Southern California, USA 

• Contributions: 

• Simplify the PI2 model-free 

reinforcement learning 

algorithm 

• Enable PI2 to simultaneously 

learn the shape and goal (i.e. 

end-point) of a motion primitive 

• Apply to learning motion 

primitives for manipulation that 

are robust to uncertainty in 

object position 

• Evaluated on a 11DOF 

manipulation platform 

14:45–14:50 MoAT8.4 

Learning Elementary Movements 

Jointly with a Higher Level Task 

Jens Kober and Jan Peters 

Department of Empirical Inference, MPI for Intelligent Systems, Germany 

Intelligent Autonomous Systems Group, TU Darmstadt, Germany 

• Learning primitive movements and higher 

level strategies at the same time. 

• Lower level learned by Cost-regularized 

Kernel Regression. 

• Transition probabilities of the higher level 

estimated from the lower level. 

• Approach evaluated on a side-stall-style 

throwing game both in simulation and with 

a real BioRob 


–17–


Learning Parameterized Policies 

Chair Jan Peters, Darmstadt Univ. of Tech. 

Co-Chair Petar Kormushev, Istituto Italiano di Tecnologia 

14:50–14:55 MoAT8.5 

Learning Interaction Control Policies 

by Demonstration 

Vasiliki Koropouli, Dongheui Lee and Sandra Hirche 

Institute of Automatic Control Engineering, 

Technical University of Munich, Germany 

• Learning interaction forces to interact with compliant environments. 

• Modeling of forces by an autonomous control policy based on parallel 

force/position control. 

• Parameterization and learning of the policy by Locally Weighted 

Regression. 

• Implemented in virtual manipulation tasks using 2DOF haptic device. 

15:00–15:15 MoAT8.7 

Adapting Control Policies for Expensive Systems to 

Changing Environments 

Matthew Tesch, Jeff Schneider and Howie Choset 

Robotics Institute, Carnegie Mellon University, United States of America 

• Optimal control parameters can vary 

significantly from environment to 

environment 

• Optimizing expensive systems is 

restrictive in even a single environment 

• Control policies define a mapping from 

environment parameters to controller 

parameters 

• Learning good policies for expensive 

systems requires reasoning about related 

environments and careful experiment 

selection 

A predicted model of an objective 

function, with the corresponding 

optimal policy. 

14:55–15:00 MoAT8.6 

Motion Generation by Reference-Point-Dependent 

Trajectory HMMs 

Komei Sugiura, Naoto Iwahashi, and Hideki Kashioka 

NICT, Japan 

• Imitation learning method for object-manipulating motions such as 

“place X on Y” and “raise Z” 

• EM algorithm and cross-validation are used for estimating the optimal 

coordinate system and state number 

• Reference-point-dependent trajectory HMM is used for generating 

smooth trajectory 

Imitation learning of motion “throw-away” 

15:15–15:30 MoAT8.8 

Online movement adaptation 

based on previous sensor experiences 

Peter Pastor1 , Ludovic Righetti1 , 

Mrinal Kalakrishnan1 , and Stefan Schaal1,2 1CLMC Lab, University of Southern California, USA 

2Max-Planck-Institute for Intelligent Systems, Tübingen, Germany 

• Stereotypical movements allow to 

accumulate previous sensor 

experiences and generate predictive 

models for subsequent task executions 

• These sensor traces are used to adapt 

the movement plan online to react to 

unexpected perturbations 

• Tight feedback integration is achieved 

using Dynamic Movement Primitives 

and a compliant control architecture 

• Our approach combines reactive 

behaviors with stereotypical 

movements to create a rich set of 

possible motions that account for 

external perturbations and perception 

uncertainty to generate truly robust 

behaviors 


–18–



Chair Han-Pang Huang, National Taiwan Univ. 

Co-Chair Stephen Mascaro, Univ. of Utah 

14:00–14:15 MoAT9.1 

Design of a New Variable Stiffness Actuator and 

Application for Assistive Exercise Control 

Tzu-Hao Huang, Jiun-Yih Kuan, and Han-Pang Huang* 

Department of Mechanical Engineering, National Taiwan University, Taiwan 

• Design concept of CCEA: antagonistically 

identical four-bar linkages with two 

extensional linear springs 

• A human-robot interaction model is 

proposed to investigate the properties of 

the system itself and its stability during 

physical human-robot interaction 

• Two control models are proposed to 

investigate the performance of assistive 

exercise control 

• CCEA exoskeleton is built for elbow 

rehabilitation. Both simulations and 

experiments are conducted to show some 

desired properties of the proposed CCEA 

system 

Fac 

M ac 

R1 

y ac 

xac 

R 2 

yca 

xca 

R1 2 R 

M ca 

14:30–14:45 MoAT9.3 

Optimal energy density piezoelectric twisting 

actuators 

Ben Finio and Robert Wood 


• Piezoelectric actuators are frequently 

useful in microrobotics applications due to 

their compact size 

• We present a torsional piezoelectric 

actuator 

• Modeling using laminate-plate theory is 

verified with experimental actuator 

performance testing 

X ac 

X 

β 

X ca 

Fl 

Y 

14:15–14:30 MoAT9.2 

A New Variable Stiffness Actuator (CompAct- 

VSA): Design and Modelling 

Nikos G. Tsagarakis, Irene Sardellitti, Darwin G. Caldwell 

Department of Advanced Robotics, Istituto Italiano di Tecnologia, Italy 

• This work introduces the mechanics, the 

principle of operation and the model of new 

variable stiffness actuator . 

• The actuator regulates the compliance 

using an amplification mechanism based 

on the lever arm principle with a 

continuously regulated pivot point. 

• The proposed principle permits the 

realization of an actuation unit with a wide 

range of stiffness and a fast stiffness 

regulation response. 

• Preliminary results are presented to 

demonstrate the fast stiffness regulation 

response and the wide range of stiffness 

achieved by the proposed design. 


–19– 

The CompAct-VSA unit 

14:45–14:50 MoAT9.4 

A Nonlinear Series Elastic Actuator for Highly 

Dynamic Motions 

• Called the HypoSEA 

• Designed specifically for force 

control of jumping robots 

• Nonlinear torque-angle 

relationship and 40J energy 

storage capability are optimized 

for hopping motions 

• Very robust against shock 

Ivar Thorson and Darwin Caldwell 

Department of Advanced Robotics, 

Istituto Italiano di Tecnologia, Italy 

The HypoSEA Actuator



Chair Han-Pang Huang, National Taiwan Univ. 

Co-Chair Stephen Mascaro, Univ. of Utah 

14:50–14:55 MoAT9.5 

Development of a 3-DOF Inchworm Mechanism 

Organized by a Pair of Y-shaped Electromagnets 

and 6 Piezoelectric Actuators 

-Design, Principle, and Experiments of Translational Motions- 

O. Fuchiwaki, M. Yatsurugi, S. Omura, and K. Arafuka 

Yokohama National University 

Hodogaya, Yokohama, Kanagawa, Japan 

• Newly developed 3-DOF inchworm 

mechanism with 6 contact points 

on a surface 

• 3-DOF simple harmonic vibration 

model to get the input signals 

• Improvement of a positioning 

repeatability, especially when it 

carries a payload 

• VTR demonstration of interesting 

omnidirectional motion is also 

available to check how it works 

Y-shaped 

electromagnet 

Piezoelectric 

actuator 

Newly-developed mechanism with 

better positioning repeatability 

15:00–15:15 MoAT9.7 

Dielectric Elastomer Bender Actuator Applied to 

Modular Robotics 

Paul White, Stella Latscha, 

Steve Schlaefer, and Mark Yim 

Mechanical Engineering and Applied Mechanics 

University of Pennsylvania, USA 

• Comparison of several actuation 

technologies with respect to important 

module metrics indicates dielectric 

elastomer actuation is a promising 

technology 

• Design, fabrication, and experimental 

characterization of a 30mm length scale 

dielectric elastomer actuated module 

• Six different modular robot configurations 

comprising two or three modules 

• Verified parallel actuation with two 

modules lifting twice the load a single 

module can 

0V 

3500V 

Single module (top); two module 

chain unactuated (middle) and 

actuated (bottom) 

14:55–15:00 MoAT9.6 

Wet Shape Memory Alloy Actuated Robotic 

Heart with Thermofluidic Feedback 

Matt Pierce and Stephen Mascaro 

Mechanical Engineering, University of Utah, USA 

• First ever successful implementation of a 

self-sustaining thermofluidically powered 

SMA pump 

• Distributes thermofluidic energy to arrays 

of SMA actuators that function as robotic 

muscles 

• Designed and optimized by varying key 

parameters through modeling, simulation, 

and experimentation 

• Capable of a net output of 66 mL/min 


–20– 

Wet SMA Robotic Heart Prototype 

15:15–15:30 MoAT9.8 

Stretchable Circuits and Sensors 

For Robotic Origami 

Jamie K Paik, Rebecca K Kramer and Robert J Wood 

Microrobotics Laboratory, 


• We present two types of stretchable 

circuits that are directly applicable to low 

profile and high strain environment of 

robotic origami: copper-mesh circuit and 

eGaIn-filled PDMS micro channel circuit. 

• Both circuits are directly connected to the 

SMA actuator module that produces 

independent folding with 0� -180� range of 

motion and 3.5mNm torque. 

• Copper-mesh circuit has strategic slits that 

are embedded within polymer to provide 

high conductivity during actuator motions. 

eGaIn circuit can produce strain up to 

160% before failure. 

Two flexible circuits integrated 

with robotic origami tile modules. 

The left shows the copper-mesh 

circuit and the right is the eGaIn 

circuit. Both circuits have trace 

widths of 1mm. For the eGaIn 

circuit, the curvature sensor is 

embedded to the right of the 

actuator.

Session MoBT1 Continental Parlor 1 Monday, September 26, 2011, 16:00–17:30 


Chair Tatsuo Arai, Osaka Univ. 

Co-Chair Jake Abbott, Univ. of Utah 

16:00–16:15 MoBT1.1 

Image-Based Magnetic Control of 

Paramagnetic Microparticles in Water 

Jasper D. Keuning, Jeroen de Vries, Leon Abelmann 

and Sarthak Misra 

University of Twente, The Netherlands 

• Implementation of a system for controlling 

the position of a 100μm paramagnetic 

microparticle using electromagnets. 

• The position of the particle is determined 

by processing microscopic images. 

• The particle is positioned with an accuracy 

of 8.4μm, achieving speeds of 235μm/s. 

• The particle can follow a circular or a 

figure-eight path. 

Image of the setup and results of the 

particle tracking a figure-eight path 

16:30–16:45 MoBT1.3 

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16:15–16:30 MoBT1.2 

Automated Micromanipulation of a Microhand 

with All-In-Focus Imaging System 

Chanh-Nghiem Nguyen, Kenichi Ohara, Ebubekir AVCI, 

Tomohito Takubo, Yasushi Mae and Tatsuo Arai 

Department of Systems Innovation, Graduate School of Engineering Science, 

Osaka University, Japan 

• In this paper, automated 

micromanipulation using a twofingered 

microhand was 

performed 

• 3D positions of both end-effectors 

and target objects were calculated 

using All-In-Focus imaging system 

• Line-Type Pattern Matching is 

proposed to calculate 3D positions 

of end-effectors 

• Automated micromanipulation was 

demonstrated with single pickand-place 

task of microspheres 


–21– 

Target microsphere 

Static microhand 

50µm 

Target is 

released 

Automated pick-and-place 

(clockwise from Top-Left) 

Target is 

picked up 

16:45–16:50 MoBT1.4 

Modeling and Design of Magnetic Sugar 

Particles Manipulation System for Fabrication of 

Vascular Scaffold 

Chengzhi Hu, Carlos Tercero, Seiichi Ikeda, Toshio Fukuda 

Department of Micro-nano Systems Engineering, Nagoya University, Japan 

Fumihito Arai 

Department of Mechanical Science and Engineering, Nagoya University, Japan 

Makoto Negoro 

Department of Neurosurgery, Fujita Health University, Japan 

• Proposing a method for developing 

magnetically controllable templates for 

particulate leaching by encapsulating 

magnetic microparticles inside sugar 

microspheres to realize 3D manipulation 

of magnetic particle used for structured 

porogen-based fabrication of vascular 

scaffolds. 

• Control methodology based on Maxwell 

coils and Helmholtz coils was illustrated 

and we theoretically analyzed the 

movement properties of MSP 

• A prototype magnetic motion control 

system for MSP/MSP cluster is presented



Chair Tatsuo Arai, Osaka Univ. 

Co-Chair Jake Abbott, Univ. of Utah 

16:50–16:55 MoBT1.5 

Toward Intuitive Teleoperation of Micro/Nano- 

Manipulators with Piezoelectric Stick-Slip Actuators 

Manikantan Nambi, Aayush Damani, and Jake J. Abbott 


• Our goal is to enable intuitive openloop 

teleoperated rate control of 

micro/nano-manipulators without 

any closed-loop feedback from the 

vision system. 

• Two algorithms are developed for 

rate control of stick-slip actuator 

based micro/nano- manipulators. 

• A method for incorporating openloop 

models of the manipulator is 

developed. 

• Experimental results for a 

positioning task in the horizontal 

plane are presented. 

(top) Experimental setup. 

(bottom) Simulation results moving from initial 

to target position. Algorithm 1 shows 

undesirable drift at very low velocities. 

17:00–17:15 MoBT1.7 

Evaluation and Application of Thermoresponsive 

Gel Handling towards Manipulation of Single Cells 

Masaru Takeuchi, Masaru Kojima and Toshio Fukuda 


Masahiro Nakajima 

Center for Micro-nano Mechatronics, Nagoya University, Japan 

• Evaluation of the Thermoresponsive Gel 

probe (GeT probe) was conducted 

• The temperature at the probe tip was 

analyzed using Finite Element Method 

(FEM) analysis 

• Fixation force was measured using AFM 

cantilever 

• Two methods were proposed to handle 

microbeads in the pure water by the GeT 

probe 

GeT probe 

Microbead 

AFM cantilever 

Thermoresponsive gel 50 �m 

50 �m 

Fixation force measurement 

using AFM cantilever 

16:55–17:00 MoBT1.6 

Pairing and Moving Swarm of Micro Particles into 

Array with a Robot-tweezer Manipulation System 

Haoyao Chen 

Mechanical Engineering and Automation, HITSZ, P.R. China 

Dong Sun 

MEEM, City University of Hong Kong, Hong Kong 

• Present the batch manipulation of 

particles by using integrated robotics and 

holographic optical tweezers technologies 

• A controller is proposed to drive pairs of 

particles to the assigned regions which 

are centered at array points. 

• The potential field method is utilized to 

avoid collisions between particles. 

• Experiments on colloidal particles are 

performed to demonstrate the 

effectiveness of the proposed approach. 

17:15–17:30 MoBT1.8 

Comparison on Experimental and Numerical 

Results for Helical Swimmers inside Channels 

Ahmet Fatih Tabak, Fatma Zeynep Temel, 

Serhat Yesilyurt 

Mechatronics Engineering, Sabanci University, Turkey 

• Magnetic swimmers with ~200 micron 

diameter Nd 2Fe 14B particles (body) and ~ 

2mm long helix-shaped rigid wires (tail). 

• 1-mm diameter glass tube filled with 

glycerol at room temperature placed 

inside Helmoltz coil arrangement. 

• Hydrodynamic model of the swimmer with 

external magnetic torque and wall effects. 

• Experimental data and simulation results 

agree well on the forward swimming 

motion inside the channel. 


–22–


Localization with Constraints 

Chair Giuseppe Oriolo, Univ. di Roma 

Co-Chair Ethan Stump, US Army Res. Lab. 

16:00–16:15 MoBT2.1 

Mutual Localization using 

Anonymous Bearing Measurements 

Paolo Stegagno*, Marco Cognetti* 

Antonio Franchi**, Giuseppe Oriolo* 

*Università di Roma “La Sapienza” 

**Max Plank Institute for Biological Cybernetics, Germany 

• Decentralized mutual localization 

problem for multi-robot systems 

• Only anonymous relative bearing 

measurements are used (no global 

localization, no distances, no identity) 

• Novel algorithm for probabilistic 

multiple registration of these 

measurements 

• Extensive experimental validation 

and comparison with the case of 

bearing-plus-distance measurements 

experiments and simulations with 

4 ground robots 

16:30–16:45 MoBT2.3 

Monte Carlo Localization using 3D Texture Maps 

Yu Fu 

National Taiwan University of Science and Technology, Taiwan 

Stephen Tully, George Kantor, and Howie Choset 

Robotics Institute, Carnegie Mellon University,USA 

• Monte Carlo Localization is 

adopted to localize a robot in a 

simplified 3D texture map 

• A simplified 3D texture map 

consists of vertical planes and 

their appearance 

• Particles converge faster using 

our method than with range-only 

MCL due to observing texture 

• Particles are more concentrated 

because the observed texture for 

a particle is generated from a 

texture map 

Localization in 3D texture map 

16:15–16:30 MoBT2.2 

Robust Local Localization for Indoor 

Environments with Uneven Floors and 

Inaccurate Maps 

Peter Abeles 

Intelligent Automation Inc., USA 

• Fault-tolerant robotic localization for indoor 

environments using Laser Range Finder 

(LRF) . 

• Unlike past work, specifically designed to 

handle inaccurate maps and uneven 

floors. 

• Novel motion estimation equations without 

singularities in long hallways. 


–23– 

Terrain at MOUT site 

16:45–16:50 MoBT2.4 

Active Target Localization 

for Bearing Based Robotic Telemetry 

Pratap Tokekar, Joshua Vander Hook and Volkan Isler 

Department of Computer Science, 

Univ. of Minnesota, USA 

• A novel robotic telemetry system 

for localizing radio-tagged invasive 

fish in frozen lakes 

• We present a technique for 

bearing-estimation from sparse 

measurements obtained from a 

directional radio antenna 

• Next, we study 3 active localization 

strategies to select sensing 

locations to minimize uncertainty in 

the location of a target 

• Our system can operate on frozen 

lakes and localize the target to 

within values as low as one meter 

The Husky robot with radio tracking 

equipment (left top of chassis) 

and antenna during field testing 

on Lake Casey, MN


Localization with Constraints 

Chair Giuseppe Oriolo, Univ. di Roma 

Co-Chair Ethan Stump, US Army Res. Lab. 

16:50–16:55 MoBT2.5 

Orientation Descriptors 

for Localization in Urban Environments 

Philip David and Sean Ho 

Army Research Laboratory, USA 

• Determine the 2D position and 3D 

orientation of a ground-level camera in 

urban environments 

• The urban model is a 2D building map 

generated from aerial imagery 

• Register building Footprint Orientation 

(FPO) descriptors calculated from the 

2D map and from omnidirectional 

ground imagery 

• Accuracy: 1m in position and 1� in 

orientation 

17:00–17:15 MoBT2.7 

Monte Carlo Localization and Registration to 

Prior Data for Outdoor Navigation 

David Silver and Anthony Stentz 


• GPS provides positioning for outdoor 

navigation, but can be intermittent 

• GPS may not be properly aligned with 

prior sources of environmental data 

• A non-parametric sensor model can be 

learned between generic onboard and 

prior data 

• The learned model can be used to localize 

in the absence of GPS, or register GPS 

and prior data 

Comparison of GPS, odometry, 

and MCL localized paths 

16:55–17:00 MoBT2.6 

A Hybrid Estimation Framework for Cooperative 

Localization under Communication Constraints 

Esha D. Nerurkar + , Ke X. Zhou*, and Stergios I. Roumeliotis + 

+ Dept. of Comp. Sci. and Engineering, Univ. of Minnesota, USA 

*Dept. of Electrical and Comp. Engineering, Univ. of Minnesota, USA 

• Develop a novel hybrid estimation scheme for CL under severe bandwidth 

constraints (robot only allowed to communicate single bit per analog msr.) 

• Proposed scheme enables each robot to process quantized msrs. from 

other robots, as well as its own analog msrs. 

• Present an improved quantization rule by using more accurate estimates 

from the hybrid estimation framework 

• Hybrid scheme achieves higher estimation accuracy than existing 

estimators that process only quantized msrs. 


–24–


Symposium: Robot Audition: From Sound Source Localization to Automatic Speech Recognition 

Chair Kazuhiro Nakadai, Honda Res. Inst. Japan Co., Ltd. 

Co-Chair Hiroshi G. Okuno, Kyoto Univ. 

16:00–16:15 MoBT3.1* 

Semi-Plenary Invited Talk: Real-Time Large Vocabulary 

Conversational Speech Recognition for Robust 

Human-Robot Interaction 

Ian Lane, Carnegie Mellon University Silicon Valley 

16:30–16:45 MoBT3.3 

Variable Frame Rate Hierarchical Analysis for 

Robust Speech Recognition 

Jean ROUAT and Stéphane LOISELLE 

NECOTIS, GEGI, Univ. de Sherbrooke, Canada 

Stéphane MOLOTCHNIKOFF 

Dépt. biologie, Univ. de Montréal, Canada 

• Extraction of acoustical events 

corresponding to ONSETS, stable, 

ascending or descending 2 nd level 

complex features 

• Events are time markers on which a 

subsequent signal frame can be placed 

for speech recognition 

• The VFR yields more than 50% increase 

in rates on TI46-words corrupted with 

Aurora-2 noises with SNR between 0 

and 20dB 

• Robustness to reverberation is shown 

16:15–16:30 MoBT3.2* 

Semi-Plenary Invited Talk: Recent Advances on Noise 

Reduction and Source Separation Technology for 

Robot Audition 

Hiroshi Saruwatari, Nara Institute of Science and Technology 

16:45–16:50 MoBT3.4 

SLAM-based Online Calibration of 

Asynchronous Microphone Array for 

Robot Audition 

1 1 

Hiroki Miura, Takami Yoshida, 

2 

1,2 

Keisuke Nakamura, Kazuhiro Nakadai 

1. Dept. of Mechanical and Environmental Informatics, 

Tokyo Institute of Techonology, Japan 

2. Honda Research Institute Japan Co., Ltd, Japan 

• We present online calibration of an 

asynchronous microphone array in 

which a location and a clock delay of each 

microphone are unknown. 

• The calibration is done by handclaps of a 

person walking around the microphone 

array with EKF-SLAM which 

simultaneously estimates a location and a 

clock delay of each microphone and a 

location of a sound source (handclapping). 

• We showed that at most 14 hand claps 

are necessary for calibration and then a 

sound source is successfully localized 

with the calibrated microphone array. 


–25– 

Mic locations and 

delay are unknown. 

Before calibration 

Calibration 

(EKF-SLAM) 

Sound 

location 

After calibration 

Spatial Spectrum of Beamforming


Symposium: Robot Audition: From Sound Source Localization to Automatic Speech Recognition 

Chair Kazuhiro Nakadai, Honda Res. Inst. Japan Co., Ltd. 

Co-Chair Hiroshi G. Okuno, Kyoto Univ. 

16:50–16:55 MoBT3.5 

HARK based Real-time Single Pane 3D Auditory 

Scene Visualizer Empowered by Speech Arrow 

Zheng Gong and Ichiro Hagiwara 

Tokyo Institute of Technology, Japan 

Kazuhiro Nakadai 



Hirofumi Nakajima 


• How to visually present information 

effectively to assist people in perception is 

a key issue for robot audition. 

• Speech Arrow is proposed to display 

recognized speeches in animated arrow 

shaped frames indicating sound source 

orientations. 

• A single pane auditory scene visualizer is 

implemented by combining visualization of 

several types of data in one window to 

increase robot auditory scene awareness. 

• The 3D vision technology is applied to 

enhance visual experiences. 

• The 3D sound is also employed to restore 

the original sound field. 

17:00–17:15 MoBT3.7 

A Scene-Associated Training Method for Mobile 

Robot Speech Recognition in Multisource 

Reverberated Environments 

Jindong Liu, Edward Johns and Guang-Zhong Yang 

The Hamlyn Centre, Imperial College London, United Kingdom 

• Aim: Train a speech recognition model 

working in varying reverberation 

environments 

• Measure room impulse 

response and group 

together similar auditory 

scenes to train a scenedependent 

speech 

recognition model 

• Utilise vision to 

recognise scenes and 

call the corresponding 

recognition model 

16:55–17:00 MoBT3.6 

Multi-modal Front-end for Speaker Activity 

Detection in Small Meetings 

Jani Even, Panikos Heracleous, Carlos Ishi and Norihiro Hagita 

ATR Intelligent Robotics and Communication Laboratories, Kyoto, Japan 

• Use LRF based human tracker for 

monitoring the movement of the 

participants 

• Assign one audio stream to each of the 

participants using the position from the 

human tracker 

• Beamforming technique is used to reduce 

background noise and interference 

• Use GMM trained before hand to detect 

speaker activity and identify the active 

speakers 

17:15–17:30 MoBT3.8 

The effects of microphone array processing on 

pitch extraction in real noisy environment 

Carlos T. Ishi, Dong Liang, Hiroshi Ishiguro, Norihiro Hagita 

ATR Intelligent Robotics and Communication Labs, Kyoto, Japan 

• Pitch extraction is important for humanrobot 

interaction, since pitch may carry 

information about intention, attitude or 

emotion expression 

• We propose a pitch extraction method 

by combining microphone array and 

auditory scene analysis technologies, 

and evaluate pitch extraction of multiple 

speakers in robot’s real noisy 

environment 


–26– 

Correlogram of baseline system 

Correlogram of proposed method 

Pitch of the target speech

Session MoBT4 Continental Ballroom 4 Monday, September 26, 2011, 16:00–17:30 

Symposium: Robot Demonstrations 

Chair Oliver Brock, Tech. Univ. Berlin 

Co-Chair Kurt Konolige, Willow Garage 


–27–


Symposium: Bio-inspired Robotics II 

Chair K. H. Low, Nanyang Tech. Univ. 

Co-Chair Ravi Vaidyanathan, Imperial Coll. London 

16:00–16:15 MoBT5.1* 

Semi-Plenary Invited Talk: Robotics at Boston 

Dynamics 

Gabriel Nelson, Boston Dynamics 

16:30–16:45 MoBT5.3 

Design of a Miniature Integrated Multi-Modal 

Jumping and Gliding Robot 

Matthew A. Woodward and Metin Sitti 

Dept. of Mechanical Engineering, Carnegie Mellon University, USA 

• Multiple locomotion modes can 

enhance a system’s performance 

in unstructured terrain. 

• A integrated strategy for 

developing systems which exhibit 

multiple locomotion modes is 

proposed. 

• A highly integrated robot is 

developed which utilizes 

approximately 80% of the system 

mass for the jumping mode and 

67% for the gliding mode. 

• This robot is capable of jumping 

heights of 6 meters with a mass of 

under 100 grams. 

16:15–16:30 MoBT5.2* 

Semi-Plenary Invited Talk: Progress on 100 Milligram 

Robotic Insects 

Robert Wood, Harvard University 

16:45–16:50 MoBT5.4 

ScarlETH: Design and Control of a Planar 

Running Robot 

Marco Hutter, C. David Remy, Mark A. Höpflinger, 

Roland Siegwart 


• Design of an articulated robotic leg 

based on high compliant series 

elastic actuation 

• Combination of versatility and 

efficiency: 

• precise torque control 

• fast position control 

• efficient energy storage 

• maximal range of motion 

• Virtual model control is applied for 

hopping in 2D: 

• virtual spring in vertical direction 

• angle of attack and force control 

in horizontal direction 


–28–


Symposium: Bio-inspired Robotics II 

Chair K. H. Low, Nanyang Tech. Univ. 

Co-Chair Ravi Vaidyanathan, Imperial Coll. London 

16:50–16:55 MoBT5.5 

Modeling and Control on Hysteresis Nonlinearity 

in Biomimetic Undulating Fins 

Tianjiang Hu, Huayong Zhu, Han Zhou, Lincheng Shen 

College of Mechatronics and Automation, Natioanl University of Defense Technology (NUDT), China 

K. H. Low 

School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore 

• Biomimetic undulating fins are considered 

with focus on their hysteresis nonlinearity, 

which causes effect on biomimetic 

investigation of fish swimming. 

• Hysteresis is confirmed with experimental 

data on RoboGnilos, and qualitative 

modeling on this nonlinear action is then 

achieved by using Preisach equations. 

• The developed iterative learning control is 

applied to eliminate hysteresis nonlinearity 

in biorobotic undulating fins. 

• Simulation and experiments show that the 

proposed control method is effective and 

feasible to improve the tracking 

performance of biorobotic fins. 

17:00–17:15 MoBT5.7 

Biologically Derived Models of the Sunfish for 

Investigations of Multi-Fin Swimming 

James Tangorra, Anthony Mignano, Gabe Carryon, and Jeff Kahn 

Department of Mechanical Engineering, Drexel University, USA 

• Biologically derived models of the 

sunfish, fin central pattern 

generators, and pectoral fin 

sensory systems were developed. 

• The biorobotic fish enables 

interdependencies of sub-systems 

to be investigated during multi-fin 

swimming. 

• Neural oscillator networks 

generate gaits for steady 

swimming and maneuvers, and 

makes stable transitions. 

• Relationships were determined 

between distributed sensory 

information and fin forces. 

Biorobotic sunfish, instrumented pectoral 

fins, and neural oscillator network. 

16:55–17:00 MoBT5.6 

Translational Damping on Flapping Cicada 

Wings 

Perry Parks, Bo Cheng, Zheng Hu, and Xinyan Deng 

School of Mechanical Engineering, Purdue University 

• Robotic insect thorax mechanism 

• Flaps up to 65Hz,and weigh 2.86g 

including the motor and wing 

• Transitional damping when body moves 

• Indirect measurement using a pendulum 

• Compared to analytical estimation from 

Blade-Element Analysis 

• Good agreement with estimation in 

forward-backward and left-right translation 

• Asymmetry in up-down translation 


–29– 

Man-made (top) and cicada 

(bottom) wings mounted on the 

high frequency robotic thorax 

mechanism. 

17:15–17:30 MoBT5.8 

Dynamic Modeling of Robotic Fish and 

Its Experimental Validation 

Jianxun Wang, Freddie Alequin-Ramos and Xiaobo Tan 

Department of Electrical and Computer Engineering, 

Michigan State University, MI, USA 

• Model carangiform robotic fish 

motion by merging rigid-body 

dynamics with Lighthill’s largeamplitude 

elongated-body theory 

• Introduce an effective approach to 

the evaluation of time-varying drag 

force and moment coefficients 

• Show that the pressure force acting 

at the tail tip plays a significant role 

• Validate the proposed approach 

with experiments on a freeswimming 

robotic fish 

A robotic fish prototype with servo-driven 

rigid tail, used for model validation


Symposium: Field Robotics II 

Chair David A. Anisi, ABB 

Co-Chair Satoshi Tadokoro, Tohoku Univ. 

16:00–16:15 MoBT6.1* 

Semi-Plenary Invited Talk: Recent Developments in 

Robotics Technology and Flight Implementation at JPL 

Richard Volpe, Jet Propulsion Laboratory, Caltech 

16:30–16:45 MoBT6.3 

Path Planning and Evaluation for Planetary 

Rovers Based on Dynamic Mobility Index 

Genya Ishigami 

Japan Aerospace Exploration Agency, Japan 

Keiji Nagatani and Kazuya Yoshida 

Tohoku University, Japan 

• Three-step path planning and 

evaluation method is proposed. 

• Step 1: Path generation on a given 

terrain map with a basic planning 

algorithm. 

• Step 2: Dynamic simulation 

providing metrics for robotic 

mobility. 

• Step 3: Path evaluation based on 

dynamic mobility index. 

• Demonstrations for the path planning 

and evaluation are presented. 

Dynamic simulation profile for 

each candidate path 

16:15–16:30 MoBT6.2 

On On-Orbit Passive Object Handling by 

Cooperating Space Robotic Servicers 

Georgios Rekleitis and Evangelos Papadopoulos 


National Technical University of Athens, Athens, Greece 

• A technique for 3D on-orbit manipulation of a passive object by 

free-flying servicers is presented 

• On-off thrusters and proportional action 

manipulators used 

• Passive object trajectory 

tracking motion strategy and 

optimal end-effector contact point selection via a two-layer optimization 

• Novel, model based controller, adapted to the special requirements is 

presented. Performance and robustness are studied via simulations 

• Comparison with standard on-off control shows reduced fuel consumption 

16:45–16:50 MoBT6.4 

Control of a Passively Steered Rover 

using 3-D Kinematics 

Neal Seegmiller and David Wettergreen 


• 3-D kinematic control for 

passively-steered rovers 

• The passive-steering design is 

reliable and efficient, but 

challenging to control 

• The 3-D controller uses inertial & 

proprioceptive sensing for 

accurate steering on rough 

terrain 

• The controller was validated in 

both simulation and physical 

experiments 


–30– 

Experiment, simulation, and diagram of the Zoë 

rover traversing an obstacle with 3-D control


Symposium: Field Robotics II 

Chair David A. Anisi, ABB 

Co-Chair Satoshi Tadokoro, Tohoku Univ. 

16:50–16:55 MoBT6.5 

Optical Flow Odometry with 

Robustness to Self-shadowing 

Neal Seegmiller and David Wettergreen 


• Efficient optical flow odometry for mobile 

robots using a single downward looking 

camera. 

• Robustness to self-shadowing errors, 

even in extreme cases when the 

shadow dominates the image. 

• Method: (1) prevent feature selection 

near shadow edges (2) outlier rejection 

based on rigidity constraint 

• Experimental results: the presented 

algorithm accurately estimates velocity 

when outlier rejection alone fails 

• Tested at 2 m/s (algorithm design permits 

higher speeds). Odometry error 2-3% of 

distance traveled. 

Tracked features in image 

dominated by the LATUV shadow 

17:00–17:15 MoBT6.7 

Time-Optimal Detumbling Maneuver 

along an Arbitrary Arm Motion 

during the Capture of a Target Satellite 

Tomohisa Oki Satoko Abiko 

MDA Space Missions, Canada Tohoku University, Japan 

Hiroki Nakanishi Kazuya Yoshida 

Japan Aerospace Exploration Agency, Japan Tohoku University, Japan 

• Time-Optimal control method to 

stabilize a detumbling satellite is 

proposed 

• Combined system dynamics between 

a free-floating robot system and a 

target satellite is presented 

• Detumbling operation along an 

arbitrary arm motion is parameterized 

by a single parameter 

• The concept of detumbling speed is 

proposed and advantageous when 

handling parameter uncertainty in the 

target dynamics 

• Numerical simulation result verifies 

the proposed control scheme 

16:55–17:00 MoBT6.6 

Vision-based Space Autonomous Rendezvous : 

A Case Study 

Antoine Petit 

IRISA/INRIA Rennes, France 

Eric Marchand 

IRISA, Université de Rennes 1, France 

Keyvan Kanani 

Astrium, France 

• 3D model-based tracking for the 

navigation of the final phase of a space 

rendezvous. 

• Tests on a mock-up of a 

satellite with a 6-DOF 

robotic arm, in open loop. 

• Precision, real-time. Robustness to 

motions, orientation, illumination. 

• 2 1/2 D visual servoing, 

using pose estimation, 

with satisfactory results. 

17:15–17:30 MoBT6.8 

3D SLAM for Planetary Worksite Mapping 

Chi Hay Tong and Timothy D. Barfoot 

Institute for Aerospace Studies, University of Toronto, Canada 

Erick Dupuis 

Space Exploration, Canadian Space Agency, Canada 

• 3D SLAM framework designed for the planetary worksite mapping task 

using a laser rangefinder mounted on a mobile rover platform 

• Utilizes a combination of sparse features and dense data for data 

association 

• Post-alignment automatic verification check to ensure map quality 

• Experimental validation at two planetary analogue test facilities 

Map of our planetary analogue test facility created by the 3D SLAM framework. 


–31–


Teleoperation 

Chair Nikhil Chopra, Univ. of Maryland, Coll. Park 

Co-Chair Patrick van der Smagt, DLR 

16:00–16:15 MoBT7.1 

A Constrained Optimization Approach to Virtual 

Fixtures for Multi-Robot Collaborative Tele-op 

Tian Xia and Peter Kazanzides and Russell Taylor 

Computer Science, Johns Hopkins University, USA 

Ankur Kapoor 

National Institute of Health, USA 

• Developed a system for roboticassisted 

manipulation using the 

da Vinci ® surgical robot 

• Used constrained optimization 

to implement multi-robot virtual 

fixtures for surgical knot 

positioning 

• Used virtual fixtures to maintain 

spatial and temporal 

relationships between robots to 

complete task 

• Used virtual fixtures provide 

haptic feedback to user 

Master 

Controller 

Slave 

Controller 

F c m 

Transformations 

- + F 

Fs 

d s 

∆X d∗ 

sl 

∆X d sl 

Collaborative 

Controller 

Master/Slave Pair 

F c m 

Transformations 

F d s 

+ 

- Fs 

∆X d sr 

∆X d∗ 

sr 

Control algorithm overview 

Master 

Controller 

Slave 

Controller 

16:30–16:45 MoBT7.3 

Small Gain Design of Cooperative Teleoperator 

System with Projection-Based Force Reflection 

Ilia Polushin and Amir Takhmar and Rajni V. Patel 


University of Western Ontario, Canada 

• Developments to the small-gain approach 

to the design of networked cooperative 

force-reflecting teleoperators are 

presented 

• Explicit assumptions on the human 

dynamics are incorporated into the 

stability analysis 

• The conservatism of the small-gain design 

is eliminated using the projeciton-based 

force reflection principle 

• Samples of the experimental results are 

presented 

Cooperative teleoperator system 

16:15–16:30 MoBT7.2 

A Task-space Weighting Matrix Approach to 

Semi-autonomous Teleoperation Control 

Pawel Malysz and Shahin Sirouspour 

Department of Electrical and Computer Engineering 

McMaster University, Canada 

• A new general framework for shared teleoperation/autonomous control 

of mobile base and/or twin-armed manipulator robots 

• Hierarchical controller based on 

task-level coordinating velocity 

commands and low-level joint 

velocity control 

• A task-space weighting matrix 

adjusts relative priority between 

autonomous and human 

teleoperation control 

• Rigorous performance and 

stability analysis 

• Experimental evaluation in 

teleoperation of a twin-armed 

Twin-armed Mobile Robot 

mobile slave robot 

16:45–16:50 MoBT7.4 

An Enhanced Sliding-Mode Control for a 

Pneumatic-Actuated Teleoperation System 

M.Q. Le1 , M.T. Pham1 , M. Tavakoli2 , R. Moreau1 1 Laboratoire Ampère, UMR CNRS 5005, Université de Lyon, France 

2 Department of Electrical and Computer Engineering, University of Alberta, Canada 

• This paper presents an enhanced sliding mode control for 

pneumatic master-slave teleoperation systems with low-cost 

solenoid valves 

• By using 2 additional modes, the system dynamic performance 

is improved and the valve’s switching activities is reduced 

• The proposed control design is experimentally verified on a 

two-channel bilateral teleoperation architecture involving 

position-position, force-force, or force-position schemes 

• Good transparency and stability were achieved 


–32– 

Pressure sensors 

Solenoid valves 

Force sensor 

Mass 

Position sensor Cylinder 

Environement


Teleoperation 

Chair Nikhil Chopra, Univ. of Maryland, Coll. Park 

Co-Chair Patrick van der Smagt, DLR 

16:50–16:55 MoBT7.5 

Subspace-oriented Energy Distribution for the 

Time Domain Passivity Approach 

Christian Ott, Jordi Artigas, and Carsten Preusche 

Institute of Robotics and Mechatronics, 

German Aerospace Center (DLR), Germany 

• Time Domain Passivity Approach applies 

a dissipative control action in order to 

preserve passivity. 

• Requires a criterion for the dissipation 

among multiple degrees of freedom. 

• We propose an energy based formulation 

for distribution of damping to task 

space and nullspace of a redundant 

manipulator. 

• The solution avoids disturbance of the 

task space, by prioritizing dissipation in 

the nullspace. 

• The method is based on an optimization 

formulation and allows to consider 

constraints in the maximum torques and 

damping values. 

passivity observer 

task space 

damping 

subspace 

damping 

distribution 

nullspace 

damping 

17:00–17:15 MoBT7.7 

Semi-Autonomous Teleoperation in Task Space 

with Redundant Slave Robot 

Yen-Chen Liu and Nikhil Chopra 

Department of Mechanical Engineering, University of Maryland, 

College Park, USA 

• Difficult to achieve efficient task space 

teleoperation of complex robots operating 

in cluttered environments. 

• Redundancy in the slave robot can be 

utilized to autonomously achieve several 

sub-tasks. 

• Control algorithms developed to 

guarantee task space tracking in the 

presence of uncertainties and delays. 

• Algorithm is validated with sub-tasks such 

as singularity avoidance, joint limits, and 

collision avoidance. 

Configurations of the slave 

robot with sub-task control 

16:55–17:00 MoBT7.6 

EMG-Based Teleoperation and Manipulation 

with the DLR LWR-III 

Jörn Vogel, Claudio Castellini and Patrick van der Smagt 

Institute of Robotics and Mechatronics, German Aerospace Center, Germany 

• Decoding wrist position, orientation 

and grasp force from surface 

electromyography 

• Control of a lightweight robotic arm 

and hand using the decoded signals 

• No kinematic model of the users arm 

or precise positioning of sEMGelectrodes 

needed 

• Possible applications in the field of 

variable stiffness robotics and 

rehabilitation 


–33– 

Schematic overview 

of the system 

17:15–17:30 MoBT7.8 

Noninvasive Brain-Computer Interface-based 

Control of Humanoid Navigation 

Yongwook Chae1 , Jaeseung Jeong2 , and Sungho Jo1 1Department of Computer Science, KAIST, Korea 

2Department of Bio and Brain Engineering, KAIST, Korea 

Key Features 

1) Natural & Direct Brain-actuated 

Navigation 

Mapped with three natural motor 

intentions into the five low level 

motion commands 

2) Direct-Control System 

Free for the external cues and 

environmental restrictions 

3) Asynchronous BCI System 

User can command the humanoid at 

any time 

4) Postural Dependent Control 

Not based on environmental 

condition (applicable to any 

environments)


Learning for Control 

Chair Jun Nakanishi, Univ. of Edinburgh 

Co-Chair Jorg Conradt, Tech. Univ. München 

16:00–16:15 MoBT8.1 

Adding a Receding Horizon to Locally Weighted 

Regression for Learning Robot Control 

Christopher Lehnert and Gordon Wyeth 

Queensland University of Technology, Australia 

• Locally Weighted Regression (LWR) 

fails to learn a useful controller when 

the system is temporally dependent. 

• We define the concept of a horizon of 

temporal dependence, and illustrate 

why LWR fails to learn. 

• By introducing a receding horizon of 

future output states of the system, we 

show that sufficient constraint is 

applied to learn good solutions. 

• Our new method, Receding Horizon 

Locally Weighted Regression, is 

demonstrated through one-shot 

learning on a real Series Elastic 

Actuator controlling a pendulum. 

Control response of Series Elastic 

Actuator after 20 seconds of training 

using Receding Horizon Locally 

Weighted Regression. 

16:30–16:45 MoBT8.3 

Learning Task-Space Control with Kernels 

D. Nguyen-Tuong1 and J. Peters1,2 1Max Planck Institute for Intelligent Systems, Germany 

2Universität Darmstadt, Intelligent Autonomous Systems, Germany 

• Learning models for task-space control 

is an ill-posed problem, i.e., learning 

one-to-many mappings. 

• We propose a local learning approach 

appropriate for learning one-to-many 

mappings. 

• The proposed approach is based on the insight that despite being a 

globally ill-posed problem, such task-space control mapping is locally 

well-defined. We show in simulation the ability of the method for online 

model learning for task-space control of redundant robots. 

16:15–16:30 MoBT8.2 

Learning Inverse Kinematics 

with Structured Prediction 

1 Botond Bócsi, 2 Duy Nguyen-Tuong, 1 Lehel Csató, 

2 Bernhard Schölkopf, 2 Jan Peters 

1 Faculty of Mathematics and Informatics, Babes-Bolyai University, Romania 

2 Dep. of Emp. Inf., Max Planck Inst. for Intelligent Sys., Tubingen, Germany 

Technische Universitaet Darmstadt, Intelligent Autonomous Systems Group, 

Germany 

• Inverse kinematics mappings are 

multivalued functions, thus, they are 

ill-defined 

• We model the joint input-output (task 

space position - joint space 

configuration) mapping and 

maximize over the output space 

• The maximization is possible using 

gradient search started from the 

current arm position 


–34– 

Inverse kinematics 

prediction scheme 

16:45–16:50 MoBT8.4 

Learning to Control Planar Hitting Motions in a 

Minigolf-like Task 

K.Kronander, S.M Khansari-Zadeh and A.Billard 

Learning Algorithms and Systems Laboratory (LASA), 

Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland 

• We present a robotic system for 

learning to play minigolf from 

human demonstrations. 

• First, the robot is taught a basic 

hitting motion using kinesthetic 

demonstrations. 

• Then, the robot learns a hitting 

motion adaptation model from 

examples provided by the 

teacher. 

• Neither physical model of the 

field or reward-function need to 

be specified. 

The 7-dof Barrett WAM playing minigolf


Learning for Control 

Chair Jun Nakanishi, Univ. of Edinburgh 

Co-Chair Jorg Conradt, Tech. Univ. München 

16:50–16:55 MoBT8.5 

Stiffness and Temporal Optimization in Periodic 

Movements: An Optimal Control Approach 

Jun Nakanishi, Konrad Rawlik and Sethu Vijayakumar 

IPAB, School of Informatics, University of Edinburgh, UK 

• Present a novel framework for stiffness 

and temporal optimization of periodic 

movements 

• Exploit the intrinsic passive dynamics to 

realize efficient actuation and control 

• Dynamical systems based representation 

of rhythmic movements 

• Propose a systematic methodology to 

optimize for control commands, temporal 

aspect of movements and time-varying 

stiffness profiles from first principles of 

optimality 

• Evaluations on a single pendulum and an 

underactuated two-link brachiating robot 

simulation 

Temporally optimized movement 

of the swing locomotion task 

17:00–17:15 MoBT8.7 

Behavioural Cloning for Driving Robots 

over Rough Terrain 

Raymond Sheh, Bernhard Hengst and Claude Sammut 

ARC Centre of Excellence for Autonomous Systems 

School of Computer Science and Engineering 

The University of New South Wales, Sydney, Australia 

• Controllers for driving robots over 

rough terrain need to consider robotterrain 

interactions that are traditionally 

difficult to model (eg. body contact). 

• We use Behavioural Cloning in 

simulation and reality to directly learn 

the control task without explicit system 

modeling. 

• We also develop an autonomous 

demonstrator to generate training data. 

• Generated policies found to transfer 

directly from simulation to reality. 

• On-task performance exceeds basic 

controllers and is comparable to the 

human expert. 

16:55–17:00 MoBT8.6 

Improving Operational Space Control of Heavy 

Manipulators via Open-Loop Compensation 

Guilherme Maeda, Surya Singh, David Rye 

Australian Centre for Field Robotics, 

The University of Sydney, Australia 

• Hydraulic manipulators present 

high friction and dynamic coupling 

making it difficult to obtain a global 

model with acceptable estimation 

error 

• Model-based compensation based 

on such models have negative 

impact on performance and may 

lead to instability 

• Open-loop commands generate 

stable partial compensation 

• In open-loop, the model can be 

local, requiring only dynamic 

knowledge in the neighborhood of 

the desired trajectory 


–35–



Chair Cagdas Denizel Onal, Massachusetts Inst. of Tech. 

Co-Chair Ohmi Fuchiwaki, Yokohama National Univ. (YNU) 

16:00–16:15 MoBT9.1 

Sliding-Mode Control of 

Nonlinear Discrete-Input Pneumatic Actuators 

Sean Hodgson 1 , Minh Quyen Le 2 , 

Mahdi Tavakoli 1 , Minh Tu Pham 2 

1 Electrical and Computer Engineering, University of Alberta, Canada 

2 Laboratoire Ampère, Université de Lyon, France 

• This paper proposes a sliding mode law 

for precise position control with minimal 

switching activity. 

• This control law is applied to 1 DOF 

pneumatic actuator utilizing on/off 

solenoid valves. 

• Utilizes 4 new modes of operation that use 

necessary and sufficient amounts of drive 

energy. 

• Simulated and experimental results 

demonstrate improved system 

performance! 

Pneumatic Actuator 

16:30–16:45 MoBT9.3 

Development of 

a Miniature Foil Type Ultrasonic Motor 

Jun Okamoto 

Institute of Advanced Biomedical Engineering & Science, 

Tokyo Women's Medical University, Japan 

Shigeki Toyama 

Department of Mechanical Systems Engineering, 

Tokyo University of Agriculture and Technology, Japan 

• A traveling wave type miniature 

ultrasonic motor using a foil type stator 

is proposed 

• It is found that the main driving force 

was the generated symmetric lamb 

wave which rotates the coil surface 

forward direction 

• Rotational speed was found to be 6,380 

rpm and starting torque was 0.698 Nm 

when the ideal gap between rotor and 

stator was realized 

• The performance of the developed 

prototype was found quite good 

compared to the earlier miniature motors 

0.8mm 

FEM Analysis 

Endurance test 

Foil type stator 

Wave 

direction 

16:15–16:30 MoBT9.2 

Trajectory Planning and Current Control Optimization 

of Three Degree-of-Freedom Spherical Actuator 

Liang Zhang, Weihai Chen, Liang Yan, and Jingmeng Liu 

School of Automation Science and Electrical Engineering, 

Beihang University, Beijing, China 

• An orientation representation 

method is proposed to 

facilitate the trajectory 

planning of rotor 

• A trajectory planning method 

which can generate smooth 

spline in two-step is utilized 

Working 

principle 

Trajectory 

planning 


–36– 

Desired torque 

Torque 

modeling 

Spherical actuator 

Current control 

optimization 

• Optimization algorithm for current is developed to improve the power 

efficiency and fault tolerance capability 

• Simulations are carried out to validate the proposed method and algorithm 

16:45–16:50 MoBT9.4 

Soft Robot Actuators using Energy-Efficient 

Valves Controlled by 

Electropermanent Magnets 

Andrew D. Marchese, Cagdas D. Onal, and Daniela Rus 

Electrical Engineering and Computer Science, MIT, USA 

•Authors employ novel 

electropermanent magnet (EPM) 

valves to drive actuation of a soft, 

multi-segment robot. 

•When used in driving soft 

actuators, EPM valves can be 

compact, light weight, and energy 

efficient. 

•The forward locomotion of a soft 

robot consisting of six compliant 

fluidic actuators was controlled by 

EPM valves. 

EPM valves were used to 

drive a six segment soft 

rolling robot. Segments 

were placed around the 

perimeter of a cylinder.



Chair Cagdas Denizel Onal, Massachusetts Inst. of Tech. 

Co-Chair Ohmi Fuchiwaki, Yokohama National Univ. (YNU) 

16:50–16:55 MoBT9.5 

Synthesis of a Non-Circular Cable Spool 

to Realize a Nonlinear Rotational Spring 

Nicolas Schmit and Masafumi Okada 

Dept. of Mech. Sciences and Eng., Tokyo Institute of Technology, Japan 

• Closed-form solution of the non-circular 

spool which synthesizes a nonlinear 

torque profile. 

• Realization of a constant force spring, an 

exponential softening spring and a cubic 

polynomial spring. 

• Average experimental error: 1.5% 

17:00–17:15 MoBT9.7 

Variable Impedance due to Electromechanical 

Coupling in Electroactive Polymer Actuators 

Sanjay Dastoor and Mark Cutkosky 

Department of Mechanical Engineering, Stanford University, USA 

• Electroactive polymers (EAPs) have 

inherent compliance and damping, making 

them attractive for bio-inspired systems 

• A novel control circuit can be used to 

change the electrical boundary conditions 

on the EAP 

• A quasi-viscoelastic model can be used to 

to simply model the dynamics of EAPs 

• Through dynamics modeling and electrical 

control, open-loop passive impedance can 

be varied, increasing the performance of 

bio-inspired robots. 

EAP actuator, variable 

impedance trajectory, and 

perching application 

16:55–17:00 MoBT9.6 

Combining IBVS and PBVS to ensure the 

visibility constraint 

Olivier Kermorgant and François Chaumette 

Lagadic, INRIA Rennes-Bretagne Atlantique, France 

• Hybrid visual servoing based on a generic fusion approach 

• Pure PBVS is performed inside a safe image area 

• 2D information is added to keep the object in the image 

• Validation with simulation and experiments 


–37– 

Visual servo with a 3D tracking 

The model nodes are used as 2D 

features when they approach the 

image border 

17:15–17:30 MoBT9.8 

Optimal Control of Multi-Input SMA Actuator 

Arrays Using Graph Theory 

Leslie Flemming, David E. Johnson and Stephen Mascaro 

Department of Mechanical Engineering, University of Utah, USA 

• Wet Shape Memory Alloy actuators are compact, high force-to-weight 

actuators that can be activated electrically or thermofluidically. 

• A scalable architecture of 2N switches/valves controls the delivery of 

thermal energy to an NxN array of actuators. 

• Expanding wavefront graph theory is used to identify optimal set of 

discrete control commands to operate the array. 

A partial graph of a 2x2 array of 

wet SMA actuators controlled by 

thermofluidic and electric inputs 

1 0 

0 0 

00 

CC 00 

EE 

B 

00 

HH 

1 0 

1 1 

EE 

EE 

HH 

HH 

0H 

00 

0E 

00 

0C 

00 

A C 

CC 

CC 

1 1 

1 1 

Legend 

Node 

Edge 

Hot Fluid 

Cold Fluid 

Electricity


–38–

8:00-9:30 



–39– 

Tuesday 


Session TuAT1 ⎯⎯ Object Recognition, Segmentation, and Detection 

Session TuAT2 ⎯⎯ Simultaneous Localization and Mapping 

Session TuAT3 ⎯⎯ Symposium: Microrobotics I 

Session TuAT4 ⎯⎯ Industrial Forum: Robots: The New Commercial Platforms 

Session TuAT5 ⎯⎯ Symposium: Medical Robotics I 

Session TuAT6 ⎯⎯ Symposium: Grasping and Manipulation: Control and Learning 

Session TuAT7 ⎯⎯ Software Architectures & Frameworks 

Session TuAT8 ⎯⎯ Bio-Inspired & Biomimetic Robots 

Session TuAT9 ⎯⎯ Probabilistic Exploration and Coverage 

10:00-11:30 

Session TuBT1 ⎯⎯ Perception, Saliency and Novelty 

Session TuBT2 ⎯⎯ Semantic SLAM & Loop Closure 

Session TuBT3 ⎯⎯ Symposium: Microrobotics II 

Session TuBT4 ⎯⎯ Industrial Forum: Robots: The Next Generation 

Session TuBT5 ⎯⎯ Symposium: Medical Robotics II 

Session TuBT6 ⎯⎯ Symposium: Grasping and Manipulation: Mechanics and Design 

Session TuBT7 ⎯⎯ Contact and Deformation 

Session TuBT8 ⎯⎯ Biomimetic Limbed Robots 

Session TuBT9 ⎯⎯ Perceptual Learning 

(continues on next page)

14:00-15:30 


–40– 

Tuesday 


(continued) 

Session TuCT1 ⎯⎯ Object Detection & Collision Avoidance 

Session TuCT2 ⎯⎯ Motion Estimation, Mapping & SLAM 

Session TuCT3 ⎯⎯ Symposium: Microrobotics III 

Session TuCT4 ⎯⎯ Forum: Robotics: Beyond the Horizon 

Session TuCT5 ⎯⎯ Symposium: Medical Robotics III 

Session TuCT6 ⎯⎯ Symposium: Grasping and Manipulation: Grasp Planning and 

Quality 

Session TuCT7 ⎯⎯ Mechanisms: Actuator Design 

Session TuCT8 ⎯⎯ Reptile & Fish-inspired Robots 

Session TuCT9 ⎯⎯ Novel Sensors

Session TuAT1 Continental Parlor 1 Tuesday, September 27, 2011, 08:00–09:30 

Object Recognition, Segmentation, and Detection 

Chair Trevor Darrell, UC Berkeley 

Co-Chair Ashley Desmond Tews, CSIRO 

08:00–08:15 TuAT1.1 

Robust Stereo-Vision Based 3D Modeling of 

Real World Objects for Assistive Robotic 

Applications 

S.K. Natarajan, D. Ristic-Durrant, A. Leu and A. Gräser 

Institute of Automation, University of Bremen, Germany 

• Novel stereo vision based segmentation of textured and uniformly 

colored objects 

• Robust closed-loop segmentation of uniformly colored segments in 

irregularly shaped object ROIs 

• Accurate 2D shape extraction for 3D object modeling 

• Needs no a-priori knowledge on object geometry 

Block Diagram of the proposed 3D Object Reconstruction System 

08:30–08:45 TuAT1.3 

Integrate Multi-Modal Cues for Category- 

Independent Object Detection and Localization 

Jianhua Zhang, Junhao Xiao and Jianwei Zhang 

Dept. Informatics, University of Hamburg, Germany 

Houxiang Zhang 

Dept. Technology and Nautical Sciences, Aalesund University College, Norway 

Shengyong Chen 

College of Computer Science, Zhejiang University of Technology, China 

• To detect and localize object instances 

that is category-independent 

• Our study uses multi-modal cues, 

including multi-scale saliency, superpixels 

straddling, intensity, depth and global 

information, into a uniform Bayesian 

framework to obtain accurate detection 

and localization. 

• Experiments show the promising 

performance of the proposed method 

based on the PASCAL VOC 08 dataset 

and our indoor scene dataset. 

Some experimental results of our 

study 

08:15–08:30 TuAT1.2 

Practical 3-D Object Detection Using Category 

and Instance-level Appearance Models 

Kate Saenko, Sergey Karayev, Yangqing Jia, Alex Shyr, Allison 

Janoch, Jonathan Long, Mario Fritz*, Trevor Darrell 

EECS, UC Berkeley, USA, *Max-Planck-Institute for Informatics, Germany 

• Effective interaction requires both 

category (e.g. bottle) and instance 

(e.g. coca-cola) detection 

• Categories are often best described 

by overall shape, instances by 

distinct local patterns (e.g. logos) 

• We combine both cues in a detection 

system based on the Kinect sensor 

• We exploit depth cues to provide 

better segmentation and object size 

constraints 

• We propose several ways to improve 

the efficiency of the search 


–41– 

shape path 

size zee constraints c 

categ category: bottle ttle 

instance: coca-cola 

08:45–08:50 TuAT1.4 

Visual and Physical Segmentation 

of Novel Objects 

Amr Almaddah, Yasushi Mae, Kenichi Ohara 

Tomohito Takubo, Tatsuo Arai 

Department of System Innovation, Osaka University, Japan 

• Visual segmentation of novel objects 

from complicated scenes by deploying 

multiple lights and objects reflectivity. 

• Physical segmentation of novel objects 

by using static electricity charge sensing 

technique. 

• By using extracted items visual and 

physical aspects, an autonomous robot 

manipulator successfully performed a 

trash separation task. 

local texture path


Object Recognition, Segmentation, and Detection 

Chair Trevor Darrell, UC Berkeley 

Co-Chair Ashley Desmond Tews, CSIRO 

08:50–08:55 TuAT1.5 

Knowing Your Limits - Self-Evaluation and 

Prediction in Object Recognition 

Michael Zillich, Johann Prankl, Thomas Mörwald, Markus Vincze 

Inst. of Automation and Control, Vienna Univ. of Technology, Austria 

• Incremental acquisition of 3D object 

models for open-ended learning scenarios 

• Probabilistic measures of observed 

detection success, predicted detection 

success, model completeness 

• Support reasoning when to extend the 

model, where to look next 

• Predict the probability of successful 

detection given the model learned so far 

Object, learned views and 

indication of model completeness 

09:00–09:15 TuAT1.7 

Generating Object Hypotheses in Natural 

Scenes through Human-Robot Interaction 

Niklas Bergström, Mårten Björkman and Danica Kragic 

CVAP/CSC, Royal Institute of Technology (KTH), Sweden 

• We present an interactive multi label, 

real time segmentation system for 

object modeling and tracking. 

• A human operator instructs the robot 

with simple commands such as ”add 

segment” or “split segment”. 

• By additionally giving relational 

information about objects, the 

segmentation can be further refined. 

• We showed through experiments 

how the different interactive elements 

helps to improve the segmentation. 

08:55–09:00 TuAT1.6 

Depth Kernel Descriptors for Object Recognition 

Liefeng Bo and Dieter Fox 

Department of Computer Science, University of Washington, USA 

Xiaofeng Ren 

Intel Labs Seattle, USA 

• Depth kernel descriptors is a general 

framework to generate rich 3D features 

• Five features over depth maps and 3D 

point clouds are proposed to capture size, 

3D shape, and edge cues for recognition 

• Evaluated on the large RGB-D Object 

dataset, consisting of 300 objects in 51 

categories for both instance and category 

object recognition 

• The proposed features are diverse yet 

complementary, as shown in the right Fig. 

• Improves over state-of-the-art features 

such as spin images. 

09:15–09:30 TuAT1.8 

3D Payload Detection From 2D Range Scans 

Ash Tews 

Autonomous Systems Laboratory, CSIRO, Australia 

• Major challenge is how to use 2D scan 

segments to classify a 3D payload 

• No prior knowledge of the payload – 

scans obtained during vehicle travel 

• System selects highly discriminating target 

scan segments for the segment reference 

library 

• New scans matched to the reference 

library and a temporal confidence value 

used for classification 

• Results demonstrated in classifying a 

smelter crucible 


–42–


Simultaneous Localization and Mapping 

Chair Jaime Valls Miro, Univ. of Tech. Sydney 

Co-Chair Denis Fernando Wolf, Univ. of Sao Paulo 

08:00–08:15 TuAT2.1 

A Hierarchical RBPF SLAM for Mobile Robot 

Coverage in Indoor Environments 

Tae-kyeong Lee and Se-young Oh 

Department of Electrical Engineering, POSTECH, Korea 

Seongsoo Lee 

Future IT Convergence Laboratory, LG Electronics Corporation, Korea 

• This paper presents a SLAM framework 

that can be applied to floor cleaning robots 

equipped with sparse and short range 

sensors 

• A line feature map is estimated based on 

RBPF, hierarchically by dividing the entire 

region into local maps 

• We improve filter performance by using an 

assumption of orthogonal environment 

• The proposed SLAM framework is 

combined with a coverage path planning 

algorithm, enabling online simultaneous 

coverage and SLAM 

(a) Map and trajectory by odometry 

(b) Map and trajectory by SLAM 

Experimental results in a real 

home environment 

08:30–08:45 TuAT2.3 

Multiple Robot Simultaneous Localization and 

Mapping 

Sajad Saeedi � , Liam Paull � , Michael Trentini �� and Howard Li � 

� Electrical and Computer Engineering, University of New Brunswick, Canada 

�� Defence Research and Development Canada, Canada 

• In this research, a decentralized platform 

for simultaneous localization and mapping 

with multiple robots has been developed 

and verified. 

• Map fusion is achieved through a multistep 

process which is based on image 

processing techniques. 

• Maps are preprocessed by Canny edge 

detection, edge smoothing and image 

segmentation. 

• Then the relative transformation matrix of 

maps is extracted by cross correlation, 

Radon transform, similarity index and 

image entropy. 

The proposed algorithm 

for the map fusion 

08:15–08:30 TuAT2.2 

Mapping of Multi-Floor Buildings: A Barometric 

Approach 

Ali Ozkil 1 , Zhun Fan 2 , Jizhong Xiao 3 , Jens Kristensen 4 

Steen Dawids 1 , Kim Christensen 4 

1: Technical University of Denmark, 2: Tongji University,China 3:The City 

College of New York, USA 4: Force Technology, Denmark 

• This paper presents a new method for 

mapping multi-floor buildings. 

• The method combines laser range 

sensor for metric mapping and 

barometric pressure sensor for detecting 

floor transitions and map segmentation. 

• We exploit the fact that the barometric 

pressure is a function of the elevation, 

and it varies between different floors. 

• The method is tested with a real robot in 

a typical indoor environment, and 

physically consistent multi-floor 

representations are achieved. 

08:45–08:50 TuAT2.4 

Improving Occupancy Grid FastSLAM 

by Integrating Navigation Sensors 

Christopher Weyers 

Sensors Directorate, Air Force Research Laboratory, USA 

Gilbert Peterson 

Department of Electrical & Computer Eng, Air Force Institute of Tech, USA 

• Simultaneous Localization and Mapping 

(SLAM) problem of constructing local grid 

maps using only internal sensors 

• Multiple Integrated Navigation Sensors 

(MINS) system combines IMU, odometry, 

stereo vision creating one input path 

• Implementation cascades MINS Kalman 

filter into FastSLAM particle filter using 

LIDAR to build 2D maps 

• MINS FastSLAM implementation 

decreased error 92% from navigation path 

and 79% from odometry path 


–43– 

Maps from Odometry, MINS, 

Odometry SLAM, MINS SLAM



Chair Jaime Valls Miro, Univ. of Tech. Sydney 

Co-Chair Denis Fernando Wolf, Univ. of Sao Paulo 

08:50–08:55 TuAT2.5 

Efficient Information-Theoretic Graph Pruning for 

Graph-Based SLAM with Laser Range Finders 

Henrik Kretzschmar, Cyrill Stachniss 


Giorgio Grisetti 

Dept. of Systems and Computer Science, Sapienza University of Rome, Italy 

• Prune the pose graph in graph-based SLAM 

• Maximize the mutual information of laser 

measurements and the resulting occupancy 

grid map 

• Discard the least informative laser scans and 

marginalize out the corresponding pose 

nodes 

• Approximate marginalization based on 

Chow-Liu trees to avoid a dense pose graph 

• Allow for long-term map learning and 

any-space SLAM 

09:00–09:15 TuAT2.7 

Neural Network-based Multiple Robot 


Sajad Saeedi � , Liam Paull � , Michael Trentini �� and Howard Li � 

� Electrical and Computer Engineering, University of New Brunswick, Canada 

�� Defence Research and Development Canada, Canada 

• In this research, a decentralized platform 

for simultaneous localization and mapping 

with multiple robots has been developed. 

• A novel occupancy grid map fusion 

algorithm is proposed. Map fusion is 

achieved through a multistep process. 

• The proposed method learns the maps 

based on the Neural Network - Self 

Organizing Map (SOM). 

• In the learning phase, the obstacles of the 

map are learned by SOM which makes 

further analyses of the map easier and 

faster. 

Two maps (top and middle) are 

fused by the proposed method 

(button) 

08:55–09:00 TuAT2.6 

An Incremental Scheme for Dictionary-based 

Compressive SLAM 

Tomomi Nagasaka and Kanji Tanaka 

Engineering, University of Fukui, Japan 

• Obtaining a compact representation of a large-size pointset map built 

by mapper robots is a critical issue for recent SLAM applications. 

• This ``map compression" problem is explored from a novel perspective 

of dictionary-based map compression in the paper. 

• The primary contribution of the 

paper is proposal of an incremental 

scheme for simultaneous mapping 

and map-compression applications. 

• An incremental map compressor 

is presented by employing a 

modified RANSAC map-matching 

scheme as well as the compact 

projection visual search. 

Incremental map-compression while 

simultaneously building the map 

09:15–09:30 TuAT2.8 

Conservative Sparsification for Efficient and Consistent 

Approximate Estimation 

John Vial and Tim Bailey 


Hugh Durrant-Whyte 

National ICT Australia 

• A new approach for sparse 

approximation. 

• Guaranteed to maintain 

consistency. 

• Maintains smaller Kullback-Leibler 

Divergence than existing 

consistent approaches. 

• Complexity is bounded by the size 

of the Markov blanket surrounding 

link removal. 

• No new nonzero links are added. 

• Elements outside Markov blanket 

area are unaffected. 


–44– 

Graph before and after sparsification. 

Light coloured link removed, no new fill 

in. Complexity is bounded by boxed 

area (may approach constant time for 

large sparse matrices).


Symposium: Microrobotics I 

Chair Sylvain Martel, Ec. Pol. de Montreal (EPM) 

Co-Chair David Cappelleri, Stevens Inst. of Tech. 

08:00–08:15 TuAT3.1* 

Semi-Plenary Invited Talk: History of Microrobotics 

and Vision for the Future: Microassembly and Beyond 

Toshio Fukuda, Nagoya University 

08:30–08:45 TuAT3.3 

Remote Microscale Teleoperation through 

Virtual Reality and Haptic Feedback 

Aude Bolopion1 , Christian Stolle2 , Robert Tunnell2 , Sinan Haliyo1 , 

Stéphane Régnier1 and Sergej Fatikow2 1 Institut des Systèmes Intelligents et de Robotique, Université Pierre et Marie 

Curie CNRS UMR 7222, France. 

2 Division Microrobotics and Control Engineering, Oldenburg University, 

Germany. 

• Teleoperation of microspheres between France and Germany is 

performed 

• Haptic feedback and 3D reconstruction of the scene are provided to 

ensure intuitiveness 

• Vision algorithms are developed to compensate the lack of force 

measurement in SEM manipulation 

• Modular software architecture and efficient transmission of the data 

ensures a wide range of applications 

08:15–08:30 TuAT3.2 

Semi-Plenary Invited Talk: History of Microrobotics 

and Vision for the Future: Microassembly and Beyond 

Toshio Fukuda, Nagoya University 

08:45–08:50 TuAT3.4 

Miniature Ferromagnetic Robot Fish Actuated by 

a Clinical Magnetic Resonance Scanner 

F. Gosselin, D. Zhou, V. Lalande, 

M. Vonthron and S. Martel 

NanoRobotics Laboratory, Ecole Polytechnique Montreal, Canada 

• A new actuation principle for a swimming 

robot is presented 

• This actuation method uses the magnetic 

field produced by a clinical MRI scanner 

• The performance of the robot fish is 

studied by varying different parameters 

• Preliminary results suggest that the 

technique could potentially be scaled 

Photograph of the actual 

down to be used for microrobots 

swimming robot compared 

with the new generation MRI 

swimmer 


–45–


Symposium: Microrobotics I 



08:50–08:55 TuAT3.5 

Hybrid Microassembly of Chips on Low 

Precision Patterns Assisted by Capillary Selfalignment 

Bo Chang, Mirva Jääskeläinen and Quan Zhou 

Department of Automation and Systems Technology, Aalto University, Finland 

• The segmented patterns having jagged 

edges are fabricated to mimic some realworld 

RFID antennas 

• Self-alignment occurs reliably despite the 

variation in size of the pattern and edge 

jaggedness 

• The alignment accuracy is likely to be 

affected when the total size of the pattern 

is different than the chip 

• The edge jaggedness seems not making 

the accuracy worse, which is a very 

interesting phenomenon 

09:00–09:15 TuAT3.7 

The Cellular Force Microscope (CFM): 

A microrobotic system for quantitating the growth 

mechanics of living, growing plant cells in situ 

Dimitrios Felekis, Simon Muntwyler, 

Felix Beyeler and Bradley J. Nelson 

Mechanical and Process Engineering, ETH Zurich, Switzerland 

• CFM is a system capable of performing 

automated mechanical characterization of 

living, growing cells in situ 

• CFM can be applied on organisms with 

changing and diverse morphology, from 

the sub-cellular to the whole organ level 

• SI-traceable calibration and 

characterization with uncertainty analysis 

is performed for the MEMS sensor 

• The results verify relevant works and 

quantify the stiffness difference between 

the tip and the shank of the cell 

CFM used to characterize single 

cell organisms 

08:55–09:00 TuAT3.6 

A Resonant Surface Sensitive to Out-of-plane Forces 

for the Indentation and Injection of Living Cells 

Denis Desmaële a,b and Mehdi Boukallel a 

a CEA, LIST, LISA, France 

Stéphane Régnier b 

b ISIR, Université Pierre et Marie Curie, France 

• This paper presents a monolithic structure 

where biological samples can be placed for 

manipulation. 

• Forces applied on samples can be 

estimated via frequency shifts of two 

resonators. 

• Static and dynamic behaviors of the force 

sensor are theoretically investigated. 

• Performances of a first prototype are 

reported. Tests on lobster eggs are also 

conducted. 

09:15–09:30 TuAT3.8 

Caging Grasps for Micromanipulation & 

Microassembly 

David J. Cappelleri, Michael Fatovic, and Zhenbo Fu 

Department of Mechanical Engineering, Stevens Institute of Technology, USA 

• Systematic method for 

defining caging grasps for 

micro-scale planar 

• Feature-Defined Cages: use 

of convex and nonconvex 

corner geometries 

• Form caging polygon to 

apply opposing force 

equivalents with probes 

• Theoretical error bounded 

analysis 

• Experimental validation of FD- 

Cages and error bounds 

• Combine with other primitives 

to execute sample assembly 

task 


–46– 

Micromanipulator 

Probes 

Fc F 

Fc c 

Fc Caging Polygon 

225 um 

[Start] 

N 4 

N 1 

225 um 

[End] 

N 2 

N 3

Session TuAT4 Continental Ballroom 4 Tuesday, September 27, 2011, 08:00–09:30 

Industrial Forum: Robots: The New Commercial Platforms 

Chair Rüdiger Dillmann, KIT Karlsruher Inst. für Tech. 

Co-Chair 


–47–


Symposium: Medical Robotics I 

Chair Jaydev P. Desai, Univ. of Maryland 

Co-Chair Paolo Fiorini, Univ. of Verona 

08:00–08:15 TuAT5.1* 

Semi-Plenary Invited Talk: Future of Surgical Robotics: 

Confluence of Surgeon demands and Technology 

Paolo Fiorini, University of Verona 

08:30–08:45 TuAT5.3 

Design of a User Interface for 

Intuitive Colonoscope Control: the Grip 

Nicole Kuperij*, Rob Reilink*, Matthijs P. Schwartz + , Stefano 

Stramigioli*, Sarthak Misra* and Ivo A.M.J. Broeders* + 

University of Twente* and Meander Medical Center + , The Netherlands 

• Colonoscopy is a medical procedure 

(screening for polyps to prevent colon 

cancer) 

• Colonoscope control is 

difficult, the control 

handle is large and not 

ergonomic 

• Procedure efficiency can 

be improved by using “the 

Grip” that actuates a 

motorized control handle 

“The Grip” controls the colonoscope tip by 

simply aiming it in the desired direction 

• “the Grip”: an innovative user interface to provide 

• Single handed control 

• Intuitive control � a 3D orientation sensor is used to map the orientation 

of the colonoscope shaft to the orientation of the tip 

• This study: colonoscope DOF and use, Grip prototype, testing on 

colonoscopy simulator (human subject experiment), promising results 

08:15–08:30 TuAT5.2 

A Prototype of Pneumatically-Driven Forceps 

Manipulator with Force Sensing Capability 

Using a Simple Flexible Joint 

Daisuke Haraguchi, Kotaro Tadano and Kenji Kawashima 

Precision and Intelligence Lab., Tokyo Institute of Technology, Japan 

• Highly simplified, 2-DOF bending distal 

joint using a high performance spring 

component is developed 

• Wire-tendon drive system is implemented 

with 4 pneumatic cylinders 

• External forces can be estimated using a 

disturbance observer (no sensors at the 

tip part) 


–48– 

Design of the forceps manipulator 

08:45–08:50 TuAT5.4 

Robot for Ultrasound-Guided 

Prostate Imaging and Intervention 

Chunwoo Kim, Felix Schäfer, Doyoung Chang, 

Doru Petrisor, Misop Han, Dan Stoianovici 

Urology Robotics Laboratory, Johns Hopkins University, U.S.A. 

• 4 degree of freedom positions and orients 

the transrectal ultrasound probe for image 

scanning and needle targeting 

• Designed to accommodate the constraints 

of the clinical prostate intervention. 

• 2D image slices are tracked and mapped 

3D volume 

• In-vitro studies on prostate mockups verify 

3D imaging capabilities. 

• Clinically used in robot-assisted 

laparoscopic radical prostatectomy for 

providing intraoperative ultrasound-based 

navigation for the surgeon. 

CAD model of the robot(top) 

Post operative 3D reconstruction 

of prostate from intraoperative 

US images (bottom)


Symposium: Medical Robotics I 



08:50–08:55 TuAT5.5 

A Modular, Mechatronic Joint Design for a 

Flexible Access Platform for MIS 

David Noonan, Valentina Vitiello, Jianzhong Shang 

Christopher Payne, Guang-Zhong Yang 

Hamlyn Centre for Robotic Surgery, Imperial College London, UK 

• Mechatronic joint module with 1-DoF or 2- 

DoF featuring embedded actuation (micro 

motor and pre-tensioned tendon) 

• Analysis of tendon path length variation, 

position repeatability and force 

transmission characteristics are presented 

• 7-DoF articulated robot constructed from 

two 2-DoF modules and three 1-DoF 

modules 

• Demonstrated diagnostic peritoneoscopy 

using probe based confocal laser 

endomicroscopy during in-vivo porcine 

trials 

7-DoF robot with three internal 

channels 

09:00–09:15 TuAT5.7 

Design of an Endoscopic Stitching Device for 

Surgical Obesity Treatment Using a N.O.T.E.S 

Approach 

Kai Xu � , Jiangran Zhao � and Minhua Zheng � 

UM-SJTU Joint Institute � and Department of Surgery � , 

Shanghai Jiao Tong University, China 

James Geiger � and Albert J. Shih � 

Department of Surgery � and Department of Mechanical Engineering � , 

University of Michigan, USA 

• This paper proposed a design of an endoscopic stitching device for 

gastroplasty using a N.O.T.E.S 

(Natural Orifice Translumenal 

Endoscopic Surgery) approach, which 

performs stitching and resizing of a 

stomach from inside the stomach. 

• Major contribution of this paper is the 

proposal of fabricating suture using 

pre-curved super-elastic NiTi alloy to 

simplify suturing motion. 

• Minor contribution of this paper is the 

presentation of one possible design to 

realize this proposed concept. 

Design of an endoscopic stitching device using 

needles made from pre-curved super-elastic 

NiTi alloy: (a) the folded configuration and (b) the unfolded working configuration. 

08:55–09:00 TuAT5.6 

Development of a “Steerable Drill” for ACL 

Reconstruction to Create the Arbitrary 

Trajectory of a Bone Tunnel 

Hiroki Watanabe, Kazuki Kanou 

Graduate school of Science and Engineering, Waseda University, Japan 

Yo Kobayashi, Masakatsu G. Fujie 

Faculty of Science and Engineering, Waseda University, Japan 

• Objective: Design of a steerable drill for ACL 

(anterior cruciate ligament) reconstruction to 

avoid LCL (lateral collateral ligament) injury. 

• Requirement: 

�Sufficient flexibility to bent to avoid LCL 

while cutting the bone. 

�Rigidity to avoid the buckle at the base 

of the drill while cutting the bone. 

• Solution: Adoption of a spring sheath with 

stiffness that varied between top and base of 

the drill to satisfy the two requirements. 

• Result: The steerable drill achieved 3 times 

the bending deformation than the drill with 

uniform stiffness as preventing the buckling. 


–49– 

Steerable drill with the nonuniform 

stiffness. 

09:15–09:30 TuAT5.8 

Active Bending Endoscope Robot System for 

Navigation through Sinus Area 

Hyun-Soo Yoon, Se Min Oh, Jin Hyeok Jeong, Seung Hwan Lee, 

Kyung Tae, and Byung-Ju Yi 

Hanyang University, Korea 

Kyoung-Chul Koh 

Sun Moon University, Korea 

• A new endoscope robot system for 

general sinus surgery is proposed. 

• The endoscope mechanism can be bent 

by 180 degrees with a small radius of 

curvature (1cm). 

• The proposed system was successfully 

implemented to inspection of a simulated 

maxillary sinus area through the nostril of 

the human silicon model without any 

collision. 

Endoscopic view 

Active Bending Endoscope Robot 

System


Symposium: Grasping and Manipulation: Control and Learning 

Chair Matei Ciocarlie, Willow Garage 

Co-Chair Peter Allen, Columbia Univ. 

08:00–08:15 TuAT6.1* 

Semi-Plenary Invited Talk: Get a Grip -- Robotic 

Dexterous Manipulation from Finger Choreography to 

The Power Pinch 

Mark Cutkosky, Stanford University 

08:30–08:45 TuAT6.3 

Synergy Level Impedance Control for 

Multifingered Hands 

Thomas Wimböck 1 , Benjamin Jahn 1,2 , and Gerd Hirzinger 1 

1 Institute of Robotics and Mechatronics, DLR, Germany 

2 Control Engineering Group, Computer Science and Automation Faculty, 

Ilmenau University of Technology, Germany 

• Controller to imitate the behaviour of a 

synergistic, respectively underactuated, 

hand. 

• Extension of the work on passivity based 

hand control at DLR. 

• Based on the analysis of the grasp 

database of the DLR Hand II. 

• Desired equilibrium in synergy coordinates 

and the corresponding synergy stiffness 

are setpoints/parameters. 

• The nullspace impedance allows to adjust 

the mechanical coupling. 

• Implementation and verification on the DLR 

Hand II. 

DLR Hand II controlled by a 

synergy impedance controller 

08:15–08:30 TuAT6.2 

Semi-Plenary Invited Talk: Get a Grip -- Robotic 

Dexterous Manipulation from Finger Choreography to 

The Power Pinch 

Mark Cutkosky, Stanford University 

08:45–08:50 TuAT6.4 

Embodiment-Specific Representation of Robot 

Grasping using Graphical Models and Latent-Space 

Discretization 

Dan Song, Carl Henrik Ek, Kai Huebner and Danica Kragic 

School of Computer Science and Communications, 

Royal Institute of Technology, Sweden 

• We propose an embodimentspecific 

representation 

framework for robot grasping. 

• To evaluate the framework, we 

train two task models, one for 

the human hand and one for 

the Armar robot hand. 

• Both models successfully 

encode the semantic task 

requirements in the continuous 

observation spaces 

• And different hand kinematics 

affect both the Bayesian 

network structure and the 

conditional distributions over 

the modeled variables 


–50–


Symposium: Grasping and Manipulation: Control and Learning 

Chair Matei Ciocarlie, Willow Garage 

Co-Chair Peter Allen, Columbia Univ. 

08:50–08:55 TuAT6.5 

Grasping Unknown Objects using an Early Cognitive 

Vision System for General Scene Understanding 

Mila Popović and Jimmy Alison Jørgensen and Norbert Krüger 

The Mærsk Institute, University of Southern Denmark, Denmark 

Gert Kootstra and Danica Kragic 

Computer Vision and Active Perception Lab, KTH, Sweden 

• Early Cognitive Vision system builds a 

hierarchical representation based on edge 

and texture information from real stereo 

images. 

• Edge-based and surface-based grasps 

are generated and tested in the simulation 

environment. 

• Method generates successful grasps, also 

for complex scenes. Edge and surface 

information are complementary. 

� The system can be used as benchmark 

for visual-based grasping. 

09:00–09:15 TuAT6.7 

Imitation Learning of Human Grasping Skills 

from Motion and Force Data 

Alexander M. Schmidts, Dongheui Lee, Angelika Peer 

Institute of Automatic Control Engineering 

Technische Universität München 

Germany 

• Imitation learning of precision grasps with multiple interaction points from 

motion and force data 

• Physical consistency between demonstrations and reproduction 

guaranteed by learning tensions compared to finger interaction forces 

• Enhanced generalisability by including force information in the learning 

process 

Teleoperation system used for demonstration. Using the data 

glove and motion tracker attached to the users hand (right) the 

simulated Elumotion Hand is controlled (left). Haptic force 

feedback is given to the user by means of a Cybergrasp. 

08:55–09:00 TuAT6.6 

Grasping of Unknown Objects via Curvature 

Maximization using Active Vision 

Berk Calli, Martijn Wisse and Pieter Jonker 

Biomechanical Engineering Department, Delft University of Technology, 

The Netherlands 

• In this paper, we propose a novel grasping 

algorithm that uses active vision as basis. 

• The algorithm does not require a 3D 

model or any offline data. 

• The algorithm uses the curvature 

information obtained from the silhouette of 

the object. 

• The silhouette is modeled using EFDs, 

and grasping points and approach vector 

have been obtained. 


–51– 

The motion that robot 

goes through for grasping. 

09:15–09:30 TuAT6.8 

Internal force control with no object motion in 

compliant robotic grasps 

Monica Malvezzi and Domenico Prattichizzo 

Department of Information Engineering, University of Siena, Italy 

Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy 

• The control of internal forces is one of the 

key issues in grasping. 

• When the robotic hand is compliant, and 

the number of controlled variables is low, it 

is possible that the control of internal 

forces implies the motion of the 

manipulated object. 

• The paper studies the structural conditions 

for the control of internal forces which do 

not involve any motion of the grasped 

object. 

• The analysis is constructive and a 

controller of internal forces is proposed.


Software Architectures & Frameworks 

Chair Geoffrey Biggs, National Inst. of AIST 

Co-Chair Roland Philippsen, Stanford Univ. 

08:00–08:15 TuAT7.1 

Intelligent System Architectures – 

Comparison by Translation 

Benjamin Dittes and Christian Goerick 

Honda Research Institute Europe GmbH, Germany 

• System architectures are important for 

constructing large software systems, 

scientific discourse requires comparability 

• We present translation to a common 

language, which we call `Systematica 2D’, 

as a way to allow comparison 

• The approach is verified on three recent, 

popular system architectures: CogX 

`George’, `ALIS3’ and the iCub Cognitive 

Architecture 

• Resulting Systematica 2D diagrams allow 

analysis of common patterns, constraints 

and design decisions 

Main information flows in ‘George’, 

‘ALIS3’ and ‘iCub’ architectures, 

derived from Systematica 2D notations 

08:30–08:45 TuAT7.3 

Analysis of Software Connectors in Robotics 

Azamat Shakhimardanov, Nico Hochgeschwender, 

Michael Reckhaus, Gerhard K. Kraetzschmar 

Bonn-Rhine-Sieg University of Applied Sciences, Germany 

• Method to analyse quality attributes in component-oriented robotic 

software 

• Protocol stack view used for the analysis of distributed robotic 

software 

• Scalability experiments with ROS and ZeroMQ 

• Lessons learned in system design for large-scale robotic applications 

08:15–08:30 TuAT7.2 

Conductor: A Controller Development 

Framework for High Degree of Freedom Systems 

Robert Sherbert and Paul Oh 

Mechanical Engineering, Drexel University, USA 

• Conductor is a software development 

framework for building controllers for high 

degree of freedom systems. 

• Conductor simplifies porting controllers 

between robots by representing hardware 

in terms of state variables. 

• Conductor can achieve significant 

increases in bandwidth usage efficiency 

within complex systems. 


–52– 

A time progression of Conductor 

running a walking trajectory on a 

high degree of freedom robot. 

08:45–08:50 TuAT7.4 

An Open Source Extensible Software Package to Create 

Whole-Body Compliant Skills in Personal Mobile Manipulators 

Roland Philippsen, Luis Sentis, Oussama Khatib 

Stanford Robotics and AI Lab, USA 

University of Texas at Austin, USA 

• Whole-body operational space 

control is a powerful compliant 

control approach for robots that 

physically interact with their 

environment 

• Open-source implementation 

with runtime configurability, 

ease of reuse and extension, 

and independence from specific 

middlewares or operating 

systems 

• Experiments on Dreamer and 

PR2 show the technical 

performance and portability. 

Visit http://stanford-wbc.sourceforge.net


Software Architectures & Frameworks 

Chair Geoffrey Biggs, National Inst. of AIST 

Co-Chair Roland Philippsen, Stanford Univ. 

08:50–08:55 TuAT7.5 

A component supervisor for RT-Middleware 

using supervision trees 

Geoffrey Biggs and Noriaki Ando and Tetsuo Kotoku 

Intelligent Systems Research Institute 

National Institute of Advanced Industrial Science and Technology 

Japan 

• Deployment and run-time management is 

an important part of a component-based 

software system. 

• We have developed a deployment and 

management tool for RT-Middleware. 

• The tool is based on the Supervision Tree e 

concept from the Erlang programming 

language. 

• The tool shows the validity of the 

Supervision Tree approach, although 

further research is needed to adapt it to 

robotics. 

09:00–09:15 TuAT7.7 

The Computing and Communication 

Architecture of the DLR Hand Arm System 

Stefan Jörg, Mathias Nickl, Alexander Nothhelfer, 

Thomas Bahls, and Gerd Hirzinger 

Inst. of Robotics and Mechatronics, German Aerospace Center, Germany 

• How to handle 52 motors and 430 

sensors? 

• How to achieve a convenient but highperformance 

interface to control the 

robot? 

• The presented solution uses a hierarchical 

net of computing nodes and combines the 

concepts of middleware and device 

drivers 

• First experiments with control applications 

(100 kHz/3KHz cycles) demonstrate the 

performance of the architecture 

08:55–09:00 TuAT7.6 

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09:15–09:30 TuAT7.8 

Caliper: A Universal Robot Simulation 

Framework for Tendon-Driven Robots 

S. Wittmeier, M. Jäntsch, K. Dalamagkidis, M. Rickert and A. Knoll 

Faculty of Informatics, TU München, Germany 

H.G. Marques 

Department of Informatics, University of Zurich, Switzerland 

• Caliper is a newly developed simulation 

framework targeted to the emerging class 

of tendon-driven robots. 

• The framework uses a modular, CORBA- 

and component-based approach. This 

provides easy extensibility and tailoring to 

meet user requirements. 

• The simulation models are defined in the 

standardized XML-based COLLADA 

format. 

• Physics-engine extensions are included to 

simulate tendon-driven actuators. 

• Caliper also provides tools for real-time 

data acquisition and visualization. 


–53– 

Caliper screenshot (top) and 

prototype of the anthropomimetic, 

tendon-driven robot ECCE-I 

(bottom)


Bio-inspired & Biomimetic Robots 

Chair Robert Wood, Harvard Univ. 

Co-Chair Howie Choset, Carnegie Mellon Univ. 

08:00–08:15 TuAT8.1 

Using Response Surfaces and Expected 

Improvement to Optimize Snake Robot Locomotion 

Matthew Tesch, Jeff Schneider and Howie Choset 

Robotics Institute, Carnegie Mellon University, United States of America 

• Goal: Improve the speed and capabilities 

of various snake robot gaits 

• Problem: Calculating the performance of a 

gait is expensive, as accurate models are 

unavailable 

• Gait optimization requires evaluations on 

an actual robot, which are expensive in 

time and resources 

• Solution: Fit a response surface as a 

surrogate objective function to assist with 

experiment selection 

08:30–08:45 TuAT8.3 

Snake-like Active Wheel Robot ACM-R4.1 

with Joint Torque Sensor and Limiter 

Shunichi Takaoka and Hiroya Yamada 

Mechanical and Aerospace Engineering, Tokyo Institute of Technology, Japan 

Shigeo Hirose 

Mechanical and Aerospace Engineering, Tokyo Institute of Technology, Japan 

• This paper proposes a snake-like active 

wheel robot named “ACM-R4.1” 

• ACM-R4.1 has a new mechanism which 

works as both a torque sensor and a 

torque limiter 

• We made theoretical consideration of 

design of the torque sensor and did 

torque measurement experiments 

• We also demonstrated the terrain 

adaptive motion by using torque sensors 

ACM-R4.1 

08:15–08:30 TuAT8.2 

State Estimation for Snake Robots 

David Rollinson, Austin Buchan and Howie Choset 


• We use an EKF to fuse 

proprioceptive data from the 

individual modules of a snake 

robot. 

• Using the Virtual Chassis 

instead of a fixed body frame 

allows more accurate estimates 

of pitch and roll. 

• State size is reduced by using 

gait parameters to approximate 

the shape of the robot during 

estimation. 

08:45–08:50 TuAT8.4 

Task-Space Control of Extensible Continuum 

Manipulators 

Apoorva Kapadia and Ian Walker 

Department of Electrical & Computer Engineering, Clemson University, USA 

• A new approach towards control of 

extensible continuum robots is presented. 

• Task-space control considered for 

continuum manipulators for first time. 

• Model-based control developed extending 

controllers for rigid-link robots. 

• Regulation of any location along the 

backbone of robot as well as tip. 


–54– 

The Octarm Grasping a 

Cylindrical Object


Bio-inspired & Biomimetic Robots 

Chair Robert Wood, Harvard Univ. 

Co-Chair Howie Choset, Carnegie Mellon Univ. 

08:50–08:55 TuAT8.5 

Novel Modal Approach for Kinematics of 

Multisection Continuum Arms 

Isuru S. Godage, Emanuele Guglielmino, David T. Branson, 

Gustavo A. Medrano-Cerda, and Darwin G. Caldwell 

Advanced Robotics Department, 

Istituto Italiano di Tecnologia, Italy. 

• Uses mode shape function (MSF) based 

approach to simplify and solve 

singularities 

• Introduces an intuitive way of deriving 

correct MSFs eliminating mode switching 

• Simulates forward kinematic spatial 

bending and pure elongation/contraction 

• Introduces inverse orientation kinematics 

for multisection continuum arms 

• Computes inverse position and/with 

orientation kinematics efficiently 

• Proposed method is applicable to a broad 

spectrum of continuum robotic arm 

designs 

Arm demonstrating inverse 

position with orientation 

kinematics in a simulated object 

inspection task 

09:00–09:15 TuAT8.7 

System identification and linear time-invariant modeling 

of an insect-sized flapping-wing micro air vehicle 

Ben Finio, Néstor Pérez-Arancibia and Robert Wood 


• Dynamic modeling of flapping-wing MAVs 

is typically highly nonlinear 

• We present a linear time-invariant (LTI) 

model and compare it to an identified 

model derived using system identification 

• We show that the linear model can 

provide an accurate estimate of system 

behavior despite inherent nonlinearities 

• Implications for vehicle design and 

optimization are discussed 

New prototype of the Harvard 

Microrobotic Fly 

08:55–09:00 TuAT8.6 

Hardware in the Loop for 

Optical Flow Sensing in a Robotic Bee 

Pierre-Emile Duhamel, Judson Porter, Ben Finio, 

David Brooks, Gu-Yeon Wei, and Robert Wood 

School of Engineering and Applied Science, Harvard University, USA 

Geoffrey Barrows 

Centeye, Inc., USA 

• Successful autonomous robotic bees 

require optimized weight and power 

consumption to increase flight time. 

• Optimizing components is challenging 

due to interconnectedness and varying 

interdisciplinary component 

development timelines. 

• Hardware in the loop (HWIL) provides 

a modular combination of robotic bee 

hardware and software components. 

• We demonstrate the value of HWIL for 

desing of an optical flow sensor. 


–55– 

Robotic Bee Hardware in the Loop 

09:15–09:30 TuAT8.8 

The acquisition of intentionally indexed and 

object centered affordance gradients 

Martí Sánchez-Fibla, Armin Duff and Paul Verschure 

SPECS, Technology Department, Universitat Pompeu Fabra (UPF), Spain 

• We introduce Affordance Gradients 

(AGs) : sensorimotor structures that 

allow to predict the consequences of 

“pushing” actions. 

• We show results in two proposed 

benchmarks: 

• (I) Pushing an object along a 

predefined trajectory 

• (II) Placing an object at a target 

position and orientation 

• Figure (a) shows a learned AG of a 

square object in Webots simulation 

environment. 

• Figure (b) shows the trajectory 

displayed by triangular and l-shaped 

pieces of benchmark (II). 

(a) 

(b)


Probabilistic Exploration and Coverage 

Chair S.K. Gupta, Univ. of Maryland 

Co-Chair Timothy Bretl, Univ. of Illinois at Urbana-Champaign 

08:00–08:15 TuAT9.1 

Exploration driven by Local Potential Distortions 

Edson Prestes and Paulo Engel 

Instituto de Informática 

Universidade Federal do Rio Grande do Sul (UFRGS) 

Brazil 

• We extend our exploration method based on BVP to allow the robot to 

explore an unknown environment dynamically while considering 

environment preferences 

• These preferences generate distortions 

in the potential field that change locally 

the concavity/convexity of the potential 

• They can be thought of as an utility 

measure that provides an estimate of the 

environment information gain and/or the 

localization accuracy 

Exploration of an unknown environment 

with high (dark gray) and low (light gray) 

preferences regions 

08:30–08:45 TuAT9.3 

Adaptive Look-Ahead for Robotic Navigation in 

Unknown Environments 

Greg Droge and Magnus Egerstedt 

Electrical and Computer Engineering 

Georgia Institute of Technology, USA 

• Schema-based behaviors allow for 

navigation based on sensor 

measurements alone. 

• We introduce a receding horizon approach 

for adaptively coordinating these 

behaviors. 

• We are operating in an unknown 

environment: not clear what the time 

horizon should be for the receding horizon 

problem. 

• We introduce a method for online 

adaptation of the time horizon based on 

how well we have predicted the state 

trajectory. 

08:15–08:30 TuAT9.2 

Histogram Based Frontier Exploration 

Amir Mobarhani, Shaghayegh Nazari, Amir H. Tamjidi, Hamid D. 

Taghirad 

Advanced Robotics and Automated Systems Lab (ARAS), Electrical and 

Computer Engineering Department, K. N. Toosi University of Technology, Iran 

• Histogram based exploration is less 

dependent on thresholds. 

• It is a weighted combination of local 

search (for closest frontiers) and global 

search (for biggest frontiers) 

• The criteria for choosing the exploration 

sub-goal is simple to implement and 

flexible. 

• Experimental results show that with low 

computational cost we can achieve better 

performance compared to the original 

paradigm of frontier exploration. 

08:45–08:50 TuAT9.4 

A receding horizon approach to generating 

dynamically feasible plans 

for vehicles that operate over large areas 

Daniel J. Stilwell and Aditya S. Gadre 

ECE Department, Virginia Tech, USA 

Andrew J. Kurdila 

ME Department, Virginia Tech, USA 

• Real-time planning of dynamically 

feasible trajectories for large operating 

environments. 

• Short-horizon trajectories are computed 

continuously. Global path is computed 

only when needed. 

• Formal guarantees that sequence of 

trajectories will converge to desired 

location 

• Suitable for vehicle systems where 

kinematic model is poor approximation of 

vehicle dynamics. 


–56–


Probabilistic Exploration and Coverage 

Chair S.K. Gupta, Univ. of Maryland 


08:50–08:55 TuAT9.5 

Planning for Landing Site Selection in the Aerial 

Supply Delivery 

Aleksandr Kushleyev and Brian MacAllister 


Maxim Likhachev 

Carnegie Mellon University, USA 

• Our goal is to generate an optimal policy 

for a UAV that minimizes the expected cost 

of flying, sensing and landing 

• Availability of landing sites is unknown 

• Modeling uncertainty in the problem 

representation allows the planner to reason 

about missing information 

• Resulting policies are more realistic, but 

complexity of the problem increases 

exponentially with number of unknowns 

• Existing PPCP framework efficiently and 

optimally solves such problems if certain 

conditions hold (Clear Preferences) 

• Our implementation provides rigorous 

theoretical guarantees and runs on a 

custom quad-rotor helicopter 

Prior terrain data 

Landing sites and sample paths 

09:00–09:15 TuAT9.7 

Probably Approximately Correct Coverage 

for Robots with Uncertainty 

Colin Das 1 , Aaron Becker 2 , and Timothy Bretl 1 

1 Aerospace Eng., University of Illinois at Urbana-Champaign, USA 

2 Electrical and Computer Eng., University of Illinois at Urbana-Champaign, USA 

• With uncertainty, it may be 

impossible to guarantee that a 

robot covers all the free space 

in finite time — how should we 

measure performance? 

• New performance measure: 

probability 1−ε of covering a 

fraction 1−δ of free space 

P(C 1 ) 1 

, [0,1] 

• Our paper shows the practical 

utility of this new performance 

measure 

robot 

area 

covered 

coverage with negligible uncertainty in 

sensing and actuation 

coverage with significant uncertainty 

in sensing and actuation 

How do we define “good performance” for coverage with uncertainty? 

08:55–09:00 TuAT9.6 

Trajectory Planning with Look-Ahead for 

Unmanned Sea Surface Vehicles to 

Handle Environmental Disturbances 

Petr Svec and Atul Thakur 

Department of Mechanical Engineering, 

University of Maryland, College Park, USA 

Maxim Schwartz 

Energetics Technology Center, USA 

Satyandra K. Gupta 

Department of Mechanical Engineering and Institute for Systems Research, 

University of Maryland, College Park, USA 

• Developed a look-ahead based 

algorithm for trajectory planning 

under motion uncertainty for 

USSVs 

• Motion uncertainty is modeled 

and explicitly used during the 

trajectory computation 

• Algorithm combines heuristic 

search with a variation of 

minimax game-tree search 

• Contingency plan is generated 

to efficiently handle sudden large 

deviations from the intended 

trajectory 


–57– 

Trajectory A: Overly conservative trajectory 

Trajectory B: Nominal trajectory computed by the finite 

horizon look-ahead planner 

Trajectory C: Contingency trajectory 

Trajectory planning under motion 

uncertainty for USSVs 

09:15–09:30 TuAT9.8 

A Dynamic Sensor Placement Algorithm for 

Dense Sampling 

Vineet Bhatawadekar, Ravishankar Sivalingam 

and Nikolaos Papanikolopoulos 

Department of Computer Science, University of Minnesota Twin Cities, USA 

• Objective: Determine the next move of 

the robot based on the spatial sampling 

density of the sensor 

• Approach: A convex optimization 

framework for determining the next-bestmove. 

• Design a convex constraint function 

based on the sampling density, and 

satisfy as many constraints as possible to 

obtain uniform and dense sampling 

Object search with our placement 

algorithm

Session TuBT1 Continental Parlor 1 Tuesday, September 27, 2011, 10:00–11:30 

Perception, Saliency and Novelty 

Chair Nicholas Gans, Univ. Texas at Dallas 

Co-Chair Michael Zillich, Vienna Univ. of Tech. 

10:00–10:15 TuBT1.1 

Multimodal Saliency-based Attention for 

Object-based Scene Analysis 

Boris Schauerte 1 , Benjamin Kühn 1 , Kristian Kroschel 2 

and Rainer Stiefelhagen 1,2 

1 Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT), Germany 

2 Fraunhofer IOSB, Germany 

• Multimodal attention is important for robots 

to operate in complex environments and 

act as social, cognitive human partners 

• Overt attention: active saliency-driven 

sensor alignment to improve the perception 

• Isophote-based saliency map 

segmentation for visual proto-objects 

• Surprise-based auditory saliency 

• Parametric 3-D saliency model enables 

efficient audio-visual saliency fusion 

• Audio-visual object validation and 

hierarchical, knowledge-driven analysis 

• Object-based inhibition of return 

Auditory Surprise (bottom) 

Fitted Gaussian models (uses Isophotebased 

Segmentation; top) and 

sequence of attentional shifts (bottom) 

10:30–10:45 TuBT1.3 

Optimisation of Gaze Movement for Multitasking 

Using Rewards 

Cem Karaoguz 1,2 , Tobias Rodemann 2 and Britta Wrede 1 

1 ) Research Institute for Cognition and Robotics (CoR-Lab), Bielefeld 

University, Germany 

2 ) Honda Research Institute Europe GmbH, Offenbach, Germany 

• A framework that optimises active scene 

perception for modular systems is 

presented. 

• Individual visual processes were 

encapsulated in modules. 

• Our framework learns how to utilise these 

modules in time for different tasks. 

• Demonstrated in a multitasking scenario 

and performed better than preprogramming 

and saliency approaches. 

10:15–10:30 TuBT1.2 

Robots Looking for Interesting Things: 

Extremum Seeking Control on Saliency Maps 

Yinghua Zhang, Jinglin Shen and Nicholas Gans 

Dept. of Electrical Engineering, University of Texas at Dallas, USA 

Mario Rotea 

Dept. of Mechanical Engineering, University of Texas at Dallas, USA 

• We present a Simplex Guided 

Extremum Seeking Control 

algorithm applied on a 6-DOF 

eye-in-hand robot arm to 

optimize the visual stimuli. 

• We use Saliency to represent 

the level of attractiveness of 

visual stimuli, which we take as 

a measurement of interest. 

• Simulations and experiments 

show the new algorithm is more 

likely to find a global maximum 

than the conventional 

Extremum Seeking Control 

10:45–10:50 TuBT1.4 

Novelty Detection Using Growing Neural Gas for 

Visuo-Spatial Memory 

Dmitry Kit and Dana Ballard 

Computer Science, University of Texas, USA 

Brian Sullivan 

Psycology, University of Texas, USA 

• Change detection is a mechanism that can 

be used as an online sensory input filter 

• A learned distribution of visuo-spatial 

features is used to detect changes 

• Trained a self-organizing map with color 

histograms and spatial data from a 1 st 

person camera 

• Online solution predicts color content for a 

viewpoint and localizes changes that 

deviate from priors 


–58– 

Change detection allows for online 

filtering so that image parsing 

computation can be constrained to 

small image regions (black regions)


Perception, Saliency and Novelty 

Chair Nicholas Gans, Univ. Texas at Dallas 

Co-Chair Michael Zillich, Vienna Univ. of Tech. 

10:50–10:55 TuBT1.5 

Coherent Spatial Abstraction and Stereo Line 

Detection for Robotic Visual Attention 

Kai Zhou, Andreas Richtsfeld, Michael Zillich and Markus Vincze 

Automation and Control Institute, Vienna University of Technology, Austria 

• Planar surfaces are estimated from 3D 

point cloud data for spatial reasoning 

• Matching edges in image pairs to compute 

stereo lines 

• Assessing planes and lines to compute 

significance values for probabilistic 

optimization 

• Refine 2D saliency map by eliminating 

wrong attentions according to spatial 

restriction and enhancing correct 

attentions wherein stereo lines are 

detected 

Up: robotic attention with 2D saliency 

Bottom: attention with combination of 

spatial and stereo line information 

11:00–11:15 TuBT1.7 

Visual Anomaly Detection under Temporal and 

Spatial Non-uniformity for News Finding Robot 

Takahiro Suzuki, Fumihiro Bessho 

Tatsuya Harada and Yasuo Kuniyoshi 

Department of Mechano-Informatics, Graduate School of Information 

Science and Technology, The University of Tokyo, Japan 

• Visual anomaly detection framework for a 

news finding robot system is proposed. 

• Non-uniform Hidden Markov Model is 

proposed to handle intermittent 

observation at each place. 

• The framework reuses information from 

similar places. Place similarity is 

automatically acquired. 

• Experimental results show that the robot 

can find news in the real world. 

A robot system based on 

proposed anomaly detection. 

10:55–11:00 TuBT1.6 

Visual Machinery Surveillance 

for High-Speed Periodic Operations 

Idaku Ishii, Yao-dang Wang and Takeshi Takaki 

Robotics Laboratory, Hiroshima University, JAPAN 

• Machinery surveillance algorithm for 

abnormal behavior detection in long-term 

abnormal behavior in a sewing machine 

t =20.589s 

periodic machinery operations in factories 

• Automatics phase encoding using several 

phase = 0.80 M0 = 34 

t =20.596s 

significant pixels for periodic operations 

phase = 1.13 M0 = 25 

• Fast abnormity detection by differencing 

input images from their synchronized 

t =20.603s 

reference images. 

• Automated HFR video logging system 

phase = 1.91 

t =20.610s 

M0 = 9 

using the machinery surveillance algorithm 

phase = 2.43 M0 = 9 

on a 1000 fps high-speed vision platform 

• Abnormal behavior of a sewing machine 

t =20.617s 

operating at 12 Hz was recorded in real 

time as a 512x512 pixel video at 1000 fps. 

phase = 3.02 M0 = 28 

t =20.624s 


–59– 

(a) input image (b) reference image 

phase = 3.61 M0 = 12 

(c) difference image 

HFR image sequence, selected reference 

images, and detected abnormal pixels 

11:15–11:30 TuBT1.8 

Representation of Manipulation-Relevant Object 

Properties and Actions 

Susanne Petsch and Darius Burschka 

Department of Informatics, Technische Universität München, Germany 

• Detection of changes in physical 

and functional properties of 

objects from unexpected human 

actions 

• Object-centric representation of 

manipulation constraints 

• Grounding of a-priori information 

to observed geometric and 

action attributes 

• Efficient monitoring system for 

detection of unexpected events 

in varying repetitive actions


Semantic SLAM & Loop Closure 

Chair Wolfram Burgard, Univ. of Freiburg 

Co-Chair Javier Civera, Univ. de Zaragoza 

10:00–10:15 TuBT2.1 

Application of Locality Sensitive Hashing to 

Realtime Loop Closure Detection 

Hossein Shahbazi and Hong Zhang 

Department of Computing Science, University of Alberta, Canada 

• Using Euclidean Locality Sensitive 

Hashing (E2LSH) for finding similar visual 

features 

• Automatic parameter tuning considering 

only the distribution of data 

• Feature matching using the distance ratio 

criterion 

• Accurate loop closure detection by 

computing image similarities 

System Overview: 

individual features are 

lookup in the map using 

LSH 

10:30–10:45 TuBT2.3 

Bathymetric SLAM with No Map Overlap using 

Gaussian Processes 

Stephen Barkby, Stefan Williams, Oscar Pizarro and Michael 

Jakuba 


• Navigation and Mapping for AUVs can 

be corrected by performing SLAM with 

multibeam sonar and a Rao 

Blackwellized Particle Filter. 

• Memory requirements significantly 

reduced by storing maps as trajectories 

linked to common bathymetry. 

• Gaussian Process Regression allows 

loop closures to be performed even 

when no map overlap is present. 

10:15–10:30 TuBT2.2 

BRIEF-GIST – 

Closing the Loop by Simple Means 

Niko Sünderhauf and Peter Protzel 

Department of Electrical Engineering and Information Technology, 

Chemnitz University of Technology, Germany 

• We propose BRIEF-Gist, a highly efficient 

scene descriptor for place recognition and 

loop closure detection. 

• We compare it to FAB-Map on two 

standard datasets. Despite its simplicity, 

BRIEF-Gist performs comparably well. 

• We perform city-scale SLAM using a novel 

optimization-based SLAM formulation that 

is robust against the few remaining falsepositive 

loop closures. 

10:45–10:50 TuBT2.4 

Place Recognition in 3D Scans Using a Combination of Bag 

of Words and Point Feature based Relative Pose Estimation 

Bastian Steder, Michael Ruhnke, 

Slawomir Grzonka, and Wolfram Burgard 

Dept.of Comp. Science, University of Freiburg, Germany 

• Place recognition based on 3D range scans. 

• A bag-of-words approach is used for a fast 

similarity pre-ordering of the scan pairs. 

• Uses NARFs (Normal Aligned 

Radial Features) to find relative 

transformations. 

• Experiments using publicly 

available datasets and 

comparison to other 

approaches. 


–60– 

Detected loop closures for the Hanover2 dataset 

(Dataset courtesy of Oliver Wulf)


Semantic SLAM & Loop Closure 

Chair Wolfram Burgard, Univ. of Freiburg 

Co-Chair Javier Civera, Univ. de Zaragoza 

10:50–10:55 TuBT2.5 

Adaptive Appearance Based Loop-Closing in 

Heterogeneous Environments 

András Majdik and Gheorghe Lazea 

Robotics Research Group,Technical University of Cluj-Napoca, Romania 

Dorian Gálvez-López and José A. Castellanos 

Instituto de Ingeniería de Aragón, University of Zaragoza, Spain 

• The paper concerns the problem of 

detecting loop-closure situations from a 

visual appearance-based perspective 

• A novel probabilistic on-line weight 

updating algorithm is proposed for the 

bag-of-words description of the gathered 

images 

• An intuitive measure of the ability of a 

certain word to contribute to the detection 

of a correct loop-closure is presented 

• The proposed strategy is extensively 

tested on challenging large-scale urban 

environments 

11:00–11:15 TuBT2.7 

Memory Management for Real-Time 

Appearance-Based Loop Closure Detection 

Mathieu Labbé and François Michaud 


Université de Sherbrooke, QC, Canada 

• Appearance-based loop closure detection 

approach that respects real-time 

constraints for large-scale SLAM 

• Memory management (Working Memory, 

Long-Term Memory) keeps computation 

time for each image acquired under a 

fixed time limit 

• Incremental approach: no prior knowledge 

of the environment is required 

• Results demonstrate the approach’s 

adaptability and scalability using four 

standard data sets 

Wait 

image 

Transfer 

(WM�LTM) 

yes 

no 

Image Location 

Process time 

over T time? 

Rehearsal 

(STM) 

Loop closure 

rejected 

Retrieval 

(LTM�WM) 

Bayesian Filter 

Update 

(WM) 

Any 

no 

hypothesis 

over Tloop ? 

yes 

Flow chart of the appearancebased 

loop closure detection 

approach 

Loop closure 

accepted 

10:55–11:00 TuBT2.6 

SLAM with Learned Object Recognition and 

Semantic Data Association 

John G Rogers III, Alexander J.B. Trevor, Carlos Nieto-Granda, 

Henrik I. Christensen 

Robotics and Intelligent Machines, Georgia Institute of Technology, USA 

• Complex and structured landmarks like 

objects have many advantages over lowlevel 

image features for semantic mapping 

• Human environments contain many objects 

which can serve as suitable landmarks for 

robot navigation 

• Maps based on high level features which 

are identified by a learned classifier could 

better inform tasks such as semantic 

mapping and mobile manipulation 

• This paper presents a technique for 

recognizing door signs using a learned 

classifier and perform semantic reasoning 

for data association 


–61– 

Large loop closed by recognizing 

and understanding semantic 

meaning of office door sign 

11:15–11:30 TuBT2.8 

Towards Semantic SLAM using a Monocular Camera 

Javier Civera, Dorian Gálvez-López, L. Riazuelo, Juan D. Tardós 

and J. M. M. Montiel 

University of Zaragoza, Spain 

• Monocular SLAM maps are usually composed of 

‘meaningless’ geometric entities (points or lines). 

• Our aim is to augment the high level information 

(semantics) in a monocular SLAM map. 

• We combine a geometric monocular SLAM 

algorithm, visual object recognition and 

registration to populate a meaningless pointbased 

map with objects. 

• Our approach runs in real-time at 7 Hz for local 

maps and a small number of objects (6 in our 

experiments). 

Image sequence and 

tracked points and objects 

Estimated camera trajectory, point 

map and inserted object 

Two objects inserted at a later step 

in the sequence


Symposium: Microrobotics II 

Chair David Cappelleri, Stevens Inst. of Tech. 

Co-Chair Sylvain Martel, Ec. Pol. de Montreal (EPM) 

10:00–10:15 TuBT3.1* 

Semi-Plenary Invited Talk: Microrobotics for 

Biomedical Applications 

Paolo Dario, Scuola Superiore Sant'Anna 

10:30–10:45 TuBT3.3 

A MRI-based Integrated Platform for the 

Navigation of Microdevices and Microrobots 

Manuel Vonthron, Viviane Lalande, Gaël Bringout 

Charles Tremblay and Sylvain Martel 

Nanorobotics Laboratory, École Polytechnique de Montréal, Canada 

• Magnetic Resonance Navigation (MRN) 

platform architecture with 3D Tracking 

and magnetic gradient based navigation 

• 3D wireless navigation with a custom 

magnetic gradient coils set providing up 

to 460mT/m 

• Experimental validation on a catheter 

with custom ferro-magnetic tip 

• Integrated platform enabling manual 

control or closed-loop control 

Superimposed deflections on a 

catheter obtained by varying a 

magnetic gradient inside a clinical MRI 

10:15–10:30 TuBT3.2 

Semi-Plenary Invited Talk: Microrobotics for 

Biomedical Applications 

Paolo Dario, Scuola Superiore Sant'Anna 

10:45–10:50 TuBT3.4 

Rotating Magnetic Micro-Robots for Versatile 

Non-Contact Fluidic Manipulation of Micro-Objects 

Eric Diller, Zhou Ye, and Metin Sitti 

Mechanical Engineering, Carnegie Mellon University, USA 

• Low Reynolds number micro-object 

manipulation by magnetic micro-robots 

• Rotational fluid flow induced to translate 

micro-objects 

• Position of microrobots is controlled by 

magnetic docks embedded in the 

substrate or by feedback control 

• High speed and precision manipulation 

is accomplished by regulating the 

spinning speed and separation distance 


–62– 

Magnetic dock concept to create 

reconfigurable virtual flow channels. The 

array of docks is embedded in the surface 

and acts to trap spinning micro-robots at 

prescribed locations to achieve controlled 

parallel fluid manipulation of micro-objects 

along complex paths


Symposium: Microrobotics II 

Chair David Cappelleri, Stevens Inst. of Tech. 

Co-Chair Sylvain Martel, Ec. Pol. de Montreal (EPM) 

10:50–10:55 TuBT3.5 

MRI Magnetic Signature Imaging, Tracking and 

Navigation for Targeted Micro/Nano-capsule 

Therapeutics 

David Folio and Antoine Ferreira 

PRISME, ENSI Bourges, France 

Christian Dahmen, Tim Wortmann and Sergej Fatikow 

AMiR, University of Oldenburg, Germany 

M. Arif Zeeshan, Kaiyu Shou, Salvador Pané, Bradley J. Nelson 

ASRL, IRIS, ETH Zurich, Switzerland 

• MRI as a medical imaging device to be 

used for targeted drug delivery 

• Tailored nanoparticles are designed as 

drug carriers with magnetic moment for 

actuation 

• MRI gradient coils propel magnetic 

objects, position feedback by artifact 

imaging and tracking of artifacts 

• Path extraction using Franqi filter and 

FMM, Navigation planning with modeled 

uncertainty 

11:00–11:15 TuBT3.7 

Design and Fabrication of 

Air-Flow based Single Particle Dispensing System 

Tomohiro Kawahara1 , Shigeo Ohashi1 , Masaya Hagiwara1 , 

Yoko Yamanishi1,2 , and Fumihito Arai1,3 1 Graduate School of Engineering, Nagoya University, Japan 

2 PRESTO, JST, Japan 

3 Seoul National University, Korea 

• Design and fabrication to increase 

the success rate of single particle 

dispensing. 

• Disposable microchip which is 

composed of two capacitance 

sensors and air-flow based inkjet 

mechanism. 

• The developed system has capability 

to eject 3 particles/sec and maximum 

flow velocity is 10 mm/s. 

• We succeed in automatic dispensing 

of a single particle (=100 um) from a 

biochip to culture well atmosphere 

with the success rate of 50 %. 

• Application to single swine oocyte 

dispensing. 

Concept 

Results 

10:55–11:00 TuBT3.6 

Tumor Targeting by Computer Controlled 

Guidance of Magnetotactic Bacteria Acting Like 

Autonomous Microrobots 

Ouajdi Felfoul, Mahmood Mohammadi, Louis Gaboury, and Sylvain Martel 

NanoRobotics Laboratory, Ecole Polytechnique Montreal, Canada 

• Magnetotactic Bacteria are controlled by 

computer as microrobots 

• A directional magnetic field is used to 

indicate the target to be reached by the 

bacteria 

• These bacteria have the capability to 

travel in the human microvasculature 

• As such, they could potentially deliver 

therapeutics inside tumors 

• Here we show that they can indeed 

penetrate solid tumors 


–63– 

Flagella 

Directional System 

MC-1 Bacterium 

11:15–11:30 TuBT3.8 

Microrobotic Simulator for Assisted Biological 

Cell Injection 

Hamid Ladjal 1,2 , Jean-Luc Hanus 2 , Antoine Ferreira 2 

1 LIRIS CNRS UMR 5205, Université Claude Bernard Lyon 1, France 

2 Laboratoire PRISME, ENSI Bourges, France 

• ICSI simulator 

• Biomechanical models for cell 

injection: 

- Linear elastic FEM 

- Hyperelastic FEM 

• 3D real-time virtual reality based 

ICSI simulator: 

- Off-line computation 

- Real-time haptics-enable simulator 

-Virtual coupling for stability of the 

haptic rendering 

• Experiments and validation 

Visual comparison between 

FEM simulations and 

experimental data.

Session TuBT4 Continental Ballroom 4 Tuesday, September 27, 2011, 10:00–11:30 

Industrial Forum: Robots: The Next Generation 

Chair Steve Cousins, Willow Garage, Inc 

Co-Chair 


–64–


Symposium: Medical Robotics II 

Chair Paolo Fiorini, Univ. of Verona 

Co-Chair Jaydev P. Desai, Univ. of Maryland 

10:00–10:15 TuBT5.1* 

Semi-Plenary Invited Talk: Challenges in MRI-guided 

Interventions 

Jaydev P. Desai, University of Maryland 

10:30–10:45 TuBT5.3 

On the Design of an Interactive, Patient-Specific 

Surgical Simulator for Mitral Valve Repair 

Neil A. Tenenholtz, Robert J. Schneider, and Robert D. Howe 


Peter E. Hammer and Nikolay V. Vasilyev 

Department of Cardiac Surgery, Children’s Hospital Boston, USA 

• A patient-specific surgical simulator 

was developed to assist in planning 

mitral valve repair. 

• Simulation occurred at 1kHz allowing for 

real-time haptic interaction with the 

virtual model. 

• The valve was simulated to closure in 

less than 1s with sub-millimeter 

accuracy. 

• A cardiac surgeon used the system to 

repair 3 pathological valves with minimal 

training. 

10:15–10:30 TuBT5.2 

Investigation of Magnetic Guidance of Cochlear 

Implants 

James R. Clark, Lisandro Leon, and Jake J. Abbott 


Frank M. Warren 

Department of Otolaryngology, Oregon Health & Science University, USA 

• Cochlear implant insertions are widely 

known to produce trauma, often leading to 

loss of residual hearing. 

• One of the goals for development of new 

electrode arrays is consistent atraumatic 

insertions. 

• We propose a concept in which a 

magnetically tipped cochlear-implant 

electrode array is guided during insertions. 

• In scaled in vitro studies, insertion forces 

were reduced by approximately 50% using 

magnetic guidance. 


–65– 

Concept for magnetically guided 

cochlear implant surgery. Red 

wide arrows indicate the three 

controlled degrees of freedom. 

10:45–10:50 TuBT5.4 

Ergonomic and Gesture Performance of 

Robotized Instruments for Laparoscopic Surgery 

Benoît Herman 1,2 , Ali Hassan Zahraee 1 , Jérôme Szewczyk 1 , 

Guillaume Morel 1 , Christophe Bourdin 3 , Jean-Louis Vercher 3 

and Brice Gayet 4 

1 ISIR, UPMC Univ Paris 06 - CNRS, France 

2 CEREM, Université catholique de Louvain - FNRS, Belgium 

3 ISM, Université de la Méditerranée - CNRS, France 

4 Department of Digestive Diseases, Montsouris Institute, France 

• New laparoscopic instrument is designed 

with robotized intra-abdominal DOFs 

and articulated handle 

• Two surgical tasks are implemented in a 

virtual reality simulator 

• A new RULA-based real-time ergonomic 

score is proposed 

• Surgeons and PhD students performed an 

experimental comparison of several 

instruments 

• The new instrument was more ergonomic 

• Design should be improved to increase 

precision


Symposium: Medical Robotics II 

Chair Paolo Fiorini, Univ. of Verona 

Co-Chair Jaydev P. Desai, Univ. of Maryland 

10:50–10:55 TuBT5.5 

Laparoscopic Optical Biopsies: In Vivo 

Robotized Mosaicing with Probe-based Confocal 

Endomicroscopy 

Benoît Rosa 1 and Benoît Herman 1,3 and Jerôme Szewczyk 1 and 

Brice Gayet 1,2 and Guillaume Morel 1 

1 ISIR, UPMC Univ Paris 06 - CNRS, France 

2 Department of Digestive Diseases, Montsouris Institute, France 

3 CEREM, Université Catholique de Louvain - FNRS, Belgium 

a – in vivo validation of the prototype 

b – computed mosaic from the acquired images 

• A minimally invasive device for performing optical biopsies 

• Microactuation using hydraulic microballoons 

• Mechanical passive physiological motion compensation 

• Preliminary in vivo validation on porcine model 

11:00–11:15 TuBT5.7 

Shape Estimation for Image-Guided Surgery with 

a Highly Articulated Snake Robot 

Stephen Tully 1 , George Kantor 2 , Marco Zenati 3 , Howie Choset 2 

1 Electrical and Computer Engineering, Carnegie Mellon University, USA 

2 Robotics Institute, Carnegie Mellon University, USA 

3 Harvard Medical School, Harvard University, USA 

• Using an EKF, we estimate the full shape 

of a surgical robot using a 5-DOF 

magnetic tracker measurement at the 

distal tip 

• For the prediction step of the filter, we 

introduce motion models for the HARP 

surgical snake robot 

• We analyze the observability of shape 

estimation for this system and show that 

given sufficient motion, the state is fully 

observable 

• To demonstrate our methods, we show 

results from an image-guidance 

experiment on a porcine subject 

Shape estimation for the HARP 

snake robot during an imageguidance 

experiment 

10:55–11:00 TuBT5.6 

Sensor and Sampling-based Motion Planning 

for Minimally Invasive Robotic Exploration 

of Osteolytic Lesions 

Wen P. Liu, Blake C. Lucas, Kelleher Guerin 

Department of Computer Science, Johns Hopkins University, USA 

Erion Plaku 

Department of Electrical Engineering and Computer Science, 

Catholic University of America, USA 

• Paper develops planning framework to 

control a flexible cannula robot to 

explore osteolytic lesions 

• Framework effectively combines global 

and local planning with information 

gain 

• Overall goal is to assist orthopaedic 

surgeons in minimally invasive 

treatment of osteolysis 

• Osteolysis is a result of bearing 

material wear in total hip replacement 


–66– 

Planner controls the snake-like 

cannula robot in minimally invasive 

treatment of osteolysis in order to 

effectively explore with its tip the 

osteolytic cavity 

11:15–11:30 TuBT5.8 

A Virtual Scalpel System for Computer-Assisted 

Laser Microsurgery 

Leonardo S. Mattos, Giulio Dagnino, Gabriele Becattini, 

Darwin G. Caldwell 

Advanced Robotics Dept., Italian Institute of Technology, Italy 

Massimo Dellepiane 

ENT Dept., UNIGE, Italy 

• Allows precise and safe laser 

control directly for live video of 

the surgical site 

• New motorized laser 

micromanipulator with 1μm 

accuracy 

• Intuitive intraoperative planning 

and automatic plan execution 

• Virtual features for increased 

safety during teleoperation 

• No operation training required 

• 50% reduction in laser aiming 

errors 

[Patient drawing from Hochman et al.]


Symposium: Grasping and Manipulation: Mechanics and Design 

Chair Aaron Dollar, Yale Univ. 

Co-Chair Aaron Edsinger, Meka Robotics 

10:00–10:15 TuBT6.1* 

Semi-Plenary Invited Talk: Preliminary Results with 

NASA's Robonaut 2 Hand 

Robert Ambrose, NASA Johnson Space Center 

10:30–10:45 TuBT6.3 

FAS A flexible Antagonistic spring element for a 

high performance over actuated hand 

Werner Friedl, Maxime Chalon, Jens Reinecke and Markus 

Grebenstein 

Institute of Robotics and Mechatronics, German Aerospace Center 

DLR Hand-Arm-System with 

opened forearm with 19 FAS 

• The FAS is an antagonistic spring element 

designed for the over actuated hand of 

the DLR hand-arm system 

• It showed its design flexibility to adapt on 

38 various stiffness characteristics and 

tendon lengths 

• The developed magnet sensor fulfils the 

design goals to be very compact and 

precise. 

• The tests show a double increase of the 

performance against the version in the 

testbed. 

10:15–10:30 TuBT6.2 

Semi-Plenary Invited Talk: Preliminary Results with 

NASA's Robonaut 2 Hand 

Robert Ambrose, NASA Johnson Space Center 

10:45–10:50 TuBT6.4 

Varying spring preloads 

to select grasp strategies in an adaptive hand 

Daniel Aukes, Barrett Heyneman, and Mark Cutkosky 

Mechanical Engineering, Stanford University, USA 

Vincent Duchaine 

Mechanical Engineering, École de Technologie Supérieure, Canada 

• Variable spring preloads allow an 

underactuated robotic hand to 

change grasping poses, 

accommodating large and small 

objects. 

• Grasp stability can be ensured for 

a larger range of object sizes 

than with a highly underactuated 

hand. 

• Grasps can be adjusted for 

situations that require higher 

precision or more safety. 


–67– 

The Seabed Rig Hand in a 

“power-pinch” grasp


Symposium: Grasping and Manipulation: Mechanics and Design 

Chair Aaron Dollar, Yale Univ. 

Co-Chair Aaron Edsinger, Meka Robotics 

10:50–10:55 TuBT6.5 

A highly-underactuated robotic hand 

with force and joint angle sensors 

Long Wang*, Joseph DelPreto**, 

Sam Bhattacharyya*. Jonathan Weisz***, 

and Peter K. Allen*** 

*Mechanical Engineering, **Electrical Engineering, ***Computer Science, 

Columbia University, USA 

• Columbia Hand – a three-fingered 

underactuated robotic hand is 

designed, prototyped, and tested 

• Highly-Underactuated – one 

motor actuates 9 DOF, while 

another actuates “Thumb” rotation 

• Position and Tactile Sensors – 

precise angle feedback and binary 

force feedback 

• Experiments – natural pre-shape 

stage; stable self-adaptive grasp 

for different shapes 

Columbia Hand 

with Position and Force sensors 

11:00–11:15 TuBT6.7 

Dynamic Nonprehensile Shaping 

of a Thin Rheological Object 

Tomoyuki Inahara, Mitsuru Higashimori, 

Kenjiro Tadakuma and Makoto Kaneko 

Department of Mechanical Engineering, Osaka University, Japan 

• A rheological object is remotely shaped on 

a plate attached at the tip of a bar. 

• A one-dimensional viscous model is 

introduced by focusing on the plastic 

deformation of the object. 

• Sufficient conditions for the plate 

accelerations to enlarge and to contract 

the object are derived. 

• Experimental results are shown for 

confirming the validity of the proposed 

shaping method. 

Nonprehensile shaping of a 

rheological object 

10:55–11:00 TuBT6.6 

Active Outline Shaping of a Rheological Object 

Based on Plastic Deformation Distribution 

Kayo Yoshimoto 

Department of Health Science, Osaka University, Japan 

Mitsuru Higashimori, Kenjiro Tadakuma, and Makoto Kaneko 

Department of Mechanical Engineering, Osaka University, Japan 

• A handling issue for a rheological object is 

discussed. 

• Based on plastic deformation distribution, 

the shaping method of the object’s outline 

by using an uniaxial gripper is proposed. 

• The desired shape outline is controlled by 

actively managing the integrated stress. 

• Experimental results by using a real 

rheological object is shown for confirming 

the validity of the proposed method. 


–68– 

Active shaping of a rheological 

object 

11:15–11:30 TuBT6.8 

Softness Effects on Manipulability and Grasp 

Stability 

Tetsuyou Watanabe 

School of Mechanical Engineering, Kanazawa University, Japan 

• Derivation of criterion index for 

manipulability including softness effect 

• Derivation of criterion index for grasp 

stability including softness effect 

• Softness effect on manipulability and 

grasp stability: the increase of the 

softness (decrease of stiffness) 

decreases the manipulability while it 

increases generable object wrench 

(grasp stability) 

Manipulability index : Im (m/s) 

0.66 

0.64 

0.62 

0.6 

Manipulability index 

0.6 

0.5 

0.4 

0.3 

Generable wrench index : If (N) 

0.58 

0.2 

Grasp stability index 

0.56 

0.1 

1 3 5 7 9 

Stiffness parameter : ki (�10000)


Contact and Deformation 

Chair Marsette Vona, Northeastern Univ. 

Co-Chair Jeff Trinkle, Rensselaer Pol. Inst. 

10:00–10:15 TuBT7.1 

Combining Imitation and Reinforcement 

Learning to Fold Deformable Planar Objects 

Benjamin Balaguer and Stefano Carpin 

School of Engineering, University of California, Merced, USA 

• Learning algorithm allowing 

cooperative manipulators to perform 

towel folding tasks; 

• Human demonstrations are 

exploited in an imitation learning 

framework to find a good starting 

seed for reinforcement learning; 

• PoWER’s reinforcement learning 

algorithm is applied to refine the 

imitation learning process and 

converge to a successful action; 

• Strengths of the algorithm are its 

efficient processing, fast learning 

capabilities, absence of a 

deformable object model, and 

applicability to other problems 

exhibiting temporally incoherent 

parameter spaces. 



10:30–10:45 TuBT7.3 

Toward Simpler Models of Bending Sheet Joints 

Lael U Odhner and Aaron M Dollar 

Department of Mechanical Engineering, Yale University, USA 

• In this paper, we introduce a 

variational model that uses only five 

parameters to describe the threedimensional 

shape of a sheet 

flexure joint 

• This model can be used to capture 

the parasitic twisting motions of the 

sheet. 

• We parameterize the twists on the 

backbone curve running along the 

length of the sheet 

• The Jacobians and Hessians of the 

joint kinematics can be computed in 

a straightforward manner, enabling 

classical robot modeling and control 

of these continuum structures 

10:15–10:30 TuBT7.2 

Bimanual Robotic Cloth Manipulation for 

Laundry Folding 

Christian Bersch, Benjamin Pitzer, Sören Kammel 

Robert Bosch Research and Technology Center North America, USA 

• Autonomous folding of garments from a 

random initial cloth configuration with a 

PR2 robot 

• Novel method for computing grasp poses 

accounting for cloth deformability 

• Automated learning of an evaluation 

function for assessment of grasp pose 

quality 


–69– 

PR2 robot manipulates T-shirt 

10:45–10:50 TuBT7.4 

Bi-Manual Robotic Paper Manipulation 

Based on Real-Time Marker Tracking and 

Physical Modelling 

Christof Elbrechter, Robert Haschke and Helge Ritter 

Neuroinfomatics Group, Bielefeld University, Germany 

• Bi-manual manipulation of paper 

using two Shadow dexterous 

robot hands (20 DOF) 

• Comparison between a purely 

mathematical approach and a 

physical modeling approach 

• Multi-camera real-time tracking in 

presence of occlusions using a 

new fiducial marker design


Contact and Deformation 

Chair Marsette Vona, Northeastern Univ. 

Co-Chair Jeff Trinkle, Rensselaer Pol. Inst. 

10:50–10:55 TuBT7.5 

Understanding the difference between prox and 

complementarity formulations for simulation of 

systems with contact 

Thorsten Schindler 

INRIA Grenoble – Rhône-Alpes, France 

Binh Nguyen, Jeff Trinkle 

Rensselaer Polytechnic Institute, USA 

• Complementarity formulations are well 

known to represent constraints in robotics 

• We present the prox formulation as an 

alternative and mathematically equivalent 

representation offering new numerical 

solution strategies 

• A study of fixed-point solution schemes 

for prox function systems shows that 

convergence depends on an additional 

parameter 

• The paradox of Painlevé illustrates that 

fixed-point schemes can fail while pivoting 

for complementarity formulations will 

succeed 

Two solutions in the paradox of 

Painlevé illustrate the fixed-point 

behavior of prox formulations 

11:00–11:15 TuBT7.7 

Estimation of Unknown Curvature using a Coarse- 

Resolution Sensor and Contact Kinematics 

Tri Cong Phung, Hansang Chae, Min Jeong Kim, Dongmin Choi, 

Seung Hoon Shin, Hyungpil Moon, Ja Choon Koo 

and Hyouk Ryeol Choi 

School of Mechanical Engineering, Sungkyunkwan University, Korea 

• Active sensing of unknown object by using 

rolling and sliding motion of fingertip 

• Applicable to an off-the-shelf sensor not 

with very high-resolution 

• Method for tracking unknown surface and 

estimating sliding motion is proposed 

• Simulations and experiments have been 

done to verify the proposed algorithm. 

Experimental setup 

10:55–11:00 TuBT7.6 

Curved Surface Contact Patches 

with Quantified Uncertainty 

Marsette Vona and Dimitrios Kanoulas 

College of Computer and Information Science, Northeastern University, USA 

• detailed models for 10 bounded curvedsurface 

patch types for contact regions 

both in the environment and on a robot 

• minimal geometric parameterizations 

using the exponential map for spatial pose 

• fast nonlinear fitting algorithm including 

quantified uncertainty both in the input 

points and the output patch 

• experimental results using Kinect 

• all sourcecode provided as the open-source 

surface patch library (SPL) 


–70– 

rock sampled with Kinect 

and 21 fitted surface patches 

11:15–11:30 TuBT7.8 

Singular surfaces and cusps in symmetric 

planar 3-RPR manipulators 

Michel Coste 

IRMAR, Université de Rennes I, France 

Philippe Wenger and Damien Chablat 

IRCCyN, CNRS - Ecole Centrale de Nantes, France 

• Moving triangle congruent to base 

triangle by indirect isometry 

• Coordinates reflecting the de-coupling 

of the DKP in two successive steps of 

degrees 3 and 2 are used 

• Precise description of the singularity 

surfaces and their cusp edges for all 

these manipulators 

• This allows sorting of assembly 

modes, used for motion planning in the 

joint space


Biomimetic Limbed Robots 

Chair Elena Garcia, Centre for Automation and Robotics - CSIC-UPM 

Co-Chair Olivier Ly, INRIA / Lab. - Bordeaux Univ. 

10:00–10:15 TuBT8.1 

Arm-Hand Movement: Imitation of Human 

Natural Gestures with Tenodesis Effect 

Kien-Cuong Nguyen and Véronique Perdereau 

UPMC Univ Paris 06, UMR 7222, ISIR, 

F-75005, FRANCE 

• Decipher natural 

movements of 

the human armhand 

system 

based on 

mechanical 

constraints 

• Elaborate the 

notion of muscle 

comfort which 

helps finding the 

human natural 

postures in the 

situation of 

redundancy 

Optimal choice of palm position in cylindrical grasp 

10:30–10:45 TuBT8.3 

Climbot: A Modular Bio-inspired Biped 

Climbing Robot 

Yisheng Guan, Li Jiang, Haifei Zhu, Xuefeng Zhou, Chuanwu Cai, 

Wenqiang Wu, Zhanchu Li, Hong Zhang and Xianmin Zhang 

Biomimetic and Intelligent Robotics Lab (BIRL) 

School of Mechanical and Automotive Engineering 

South China University of Technology, Guangzhou, China 

�A modular bio-inspired biped climbing 

robot - Climbot is developed for highrise 

work in agriculture, forestry and 

construction 

� The robot has both climbing and 

manipulating functions 

�The mechanical and control system of 

the robot are introduced 

�Three climbing gaits – inchworm gait, 

swinging-around gait and flipping-over 

gait – are illustrated with experiments 

�An application is also demonstrated 

Climbot unscrewing a light-bulb 

10:15–10:30 TuBT8.2 

Bio-inspired vertebral column, compliance and semipassive 

dynamics in a lightweight humanoid robot 

Olivier Ly 

INRIA / LaBRI – Bordeaux University - France 

and Matthieu Lapeyre and Pierre-Yves Oudeyer 

Flowers – INRIA Bordeaux - France 

• Compliance and semi-passive dy- 

namics for locomotion of humanoid 

robots. 

• Robustness against unknown 

external perturbations. 

• The advantages of a bio-inspired 

multi-articulated vertebral column. 


–71– 

Compliance of the Acroban Robot 

10:45–10:50 TuBT8.4 

Passive undulatory gaits enhance walking in a 

myriapod millirobot 

Katie L. Hoffman and Robert J. Wood 


• Design and modeling of a 

centipede millirobot with a 

compliant body and passive 

intersegmental connections 

• Body undulations result from 

changing phase of stance between 

adjacent segments in simulation 

and experiments 

• These undulations enhance 

locomotion when compared to nonundulatory 

gaits 

• The model and millirobot can be 

used to study aspects of biological 

myriapod locomotion 

Photo of a 10-segment, 20-leg 

centipede millirobot


Biomimetic Limbed Robots 

Chair Elena Garcia, Centre for Automation and Robotics - CSIC-UPM 

Co-Chair Olivier Ly, INRIA / Lab. - Bordeaux Univ. 

10:50–10:55 TuBT8.5 

Mechanical Design of a Tree Gripper for 

Miniature Tree-Climbing Robots 

Tin Lun Lam and Yangsheng Xu 

Department of Mechanical and Automation Engineering, The Chinese 

University of Hong Kong, Hong Kong, China 

• Development of a claw-based gripper for 

miniature tree-climbing robots 

• Capable of attaching on a wide variety of 

trees with a wide range of gripping 

curvature 

• Actuated by one actuator 

• Lightweight and simple in control 

• Allows zero energy consumption in static 

gripping 

A tree-climbing robot - Treebot 

11:00–11:15 TuBT8.7 

Neural-Body Coupling for Emergent 

Locomotion: a Musculoskeletal Quadruped 

Robot with Spinobulbar Model 

Yasunori Yamada, Satoshi Nishikawa, Kazuya Shida 

and Yasuo Kuniyoshi 

Grad. School of Info. Sci. & Tech., the Univ. of Tokyo, Japan 

Ryuma Niiyama 

Computer Science and Artificial Intelligence Lab, MIT, USA 

• The robot with biologically-realistic 

features in actuator and its configuration, 

sensor and nervous system 

• Quantifying the contribution of the 

morphology in structuring sensorimotor 

information via embodied interaction 

• Various and coordinated locomotion 

emerging from dynamic interaction with 

no pre-defined coordination circuit 

Robot and muscle configuration. 

10:55–11:00 TuBT8.6 

Bio-inspired Step Crossing Algorithm 

for a Hexapod Robot 

Ya-Cheng Chou, Wei-Shun Yu, Ke-Jung Huang, and Pei-Chun Lin 

Department of Mechanical Engineering, National Taiwan University, TAIWAN 

• The algorithm is inspired by the 

observation that the cockroach changes 

the tripod gait to a special gait to cross the 

high steps 

• Gait is composed by two stages: 

• “Rearing stage” to lift the front side of 

the body 

• “Lifting stage” to maneuver the body 

COM to pass the edge of the step 

• With the inclinometer feedback, the robot 

can automatically adjust its gait for 

crossing steps with different heights 

• The performance of the algorithm is 

experimentally evaluated 

11:15–11:30 TuBT8.8 

Design and development of a biomimetic leg 

using hybrid actuators 

Elena Garcia, Juan C. Arevalo, Fernando Sanchez, Javier F. 

Sarria and Pablo Gonzalez-de-Santos 

Centre for Automation and Robotics, 

Spanish National Research Council, Spain 

• The design, development and preliminary 

tests of a robotic leg for agile locomotion 

is described. 

• The multidisciplinary performance of the 

natural muscle is imitated by combination 

of technologies. 

• The actuation system is based on the 

hybrid use of series elasticity and 

magneto-rheological dampers . 

• Experiments with the real leg prototype 

show a natural-looking motion following 

reference data from CGA . 


–72– 

The HADE leg prototype


Perceptual Learning 

Chair Danica Kragic, KTH 


10:00–10:15 TuBT9.1 

Learning spatial relations 

from functional simulation 

Kristoffer Sjöö and Patric Jensfelt 

Centre for Autonomous Systems, 

Royal Institute of Technology, Sweden 

• Understanding spatial relationships will be 

important for autonomous robots in 

complex environments 

• We submit that functional distinctions are 

crucial for conceptualizing spatial relations 

• 5 different functional distinctions are 

learned from data obtained through 

physics simulation 

• Resulting models can successfully predict 

qualitative action outcomes 

10:30–10:45 TuBT9.3 

Online Multiple Instance Learning Applied to 

Hand Detection in a Humanoid Robot 

Carlo Ciliberto 1 , Fabrizio Smeraldi 2 

Lorenzo Natale 1 , Giorgio Metta 1 

1) RBCS, Istituto Italiano di Tecnologia, Italy 

2) EECS, Queen Mary University of London, United Kingdom 

• We propose an online algorithm for the 

visual detection and localisation of the 

hand of the iCub robot that imposes low 

requirements on supervision. 

• Learning is achieved by online boosting 

using ‘Multiple Instance’ weak learners 

implemented as balls in feature space. 

• The weak learners are wrapped by 

‘selectors’ to allow for feature selection. 

• Coarse labeling strategies (e.g. motion) 

can be used for self-supervised 

learning. 

• To the best of our knowledge, this is the 

first application of online Multiple 

Instance Learning in robotics. 

Examples of localisation for the 

hand of the iCub humanoid robot 

10:15–10:30 TuBT9.2 

Multimodal Categorization 

by Hierarchical Dirichlet Process 

Tomoaki Nakamura, Takayuki Nagai 

Dept. of Elec. Eng., The Univ. of Electro-Communications, Japan 

Naoto Iwahashi 

NICT Knowledge Creating Communication Research Center, Japan 

• A nonparametric Bayesian framework for 

unsupervised object categorization by a 

robot 

• Multimodal (visual, audio and haptic) 

information is utilized for the 

categorization 

• Hierarchical Dirichlet Process (HDP) is 

extended to multimodal HDP 

It enables the system to estimate 

the number of categories 

• The proposed method provides a 

probabilistic framework for inferring 

object properties from limited 

observations 


–73– 

Objects 

Multimodal 

Information 

Category1 Category2 Category3 

10:45–10:50 TuBT9.4 

Active learning using a Variational Dirichlet 

Process model for pre-clustering and 

classification of underwater stereo imagery 

Ariell Friedman, Daniel Steinberg, 

Oscar Pizarro and Stefan B. Williams 

Australian Centre for Field Robotics, The University of Sydney, Australia 

• Active learning using a Variational 

Dirichlet Process (VDP) model: 

- Pre-clustering & classification 

- Minimises human effort for training & 

improves classification accuracy 

- Utilises structure in unlabelled data 

• Features: 3D morphology combined with 

colour and image texture 

• Comparison: similar implementations 

using Expectation Maximisation (EM) 

and Naive Bayes classifier (NB) 

• Result: fewer labels are required to 

achieve a given level of accuracy 

Mean accuracy 

Std dev 

Least L t confident fid t sampling li with ithdiff different t classifiers l ifi 

1 

0.8 

0.6 

10 0 

0.4 

10 0 

0.2 

0.1 

0 

10 1 

10 2 

10 1 

10 2 

# labeled instances (log scale) 

EM 

NB 

VDP 

10 3 

10 3


Perceptual Learning 

Chair Danica Kragic, KTH 


10:50–10:55 TuBT9.5 

Autonomous Acquisition of Multimodal Information 

for Online Object Concept Formation by Robots 

Takaya Araki, Tomoaki Nakamura and Takayuki Nagai 

Dept. of Elec. Eng., The Univ. of Electro-Communications, Japan 

Kotaro Funakoshi and Mikio Nakano 


Naoto Iwahashi 

NICT Knowledge Creating Communication Research Center, Japan 

• A novel framework for acquisition of 

multimodal information and concept 

formation by robots 

• The robot autonomously acquires haptic, 

audio and visual information by grasping, 

shaking and observing the target object 

• The algorithm of multimodal categorization 

using Gibbs sampling is extended to an 

online version 

Online Multimodal LDA 

• This framework enables inference, 

classification, and the model update 

simultaneously 

Autonomous 

Information 

Acquisition 

Multimodal object concepts 

Category 1 

Words 

Plushie, Soft, 

Animal 

Category 2 

Words 

Cup, Hard, 

Handle 

Category 3 

Words 

Plastic bottle, 

Hard, Liquid 

11:00–11:15 TuBT9.7 

�� 

�� 

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�� 

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� �� 

�� 

�� 

� �� 

�� 

�� 

� �� 

�� 

�� 

�� 

�� 

�� 

10:55–11:00 TuBT9.6 

Learning Robot Grasping from 3-D Images with 

Markov Random Fields 

Abdeslam Boularias, Oliver Kroemer, Jan Peters 

Max-Planck Institute for Intelligent Systems, Tuebingen, Germany 

• Use a depth camera 

(Kinect,Swissranger, .. ) to create a 3D 

image of an object (a point cloud). 

• Transform the point cloud to a knearest 

neighbor graph. 

• The graph defines a Markov Random 

Field, where the vertices are labeled as 

“good” or “bad” grasping points. 

• The parameters of the Network are 

learned by using grasping examples 

provided by a human. 

• A new object is grasped by classifying 

its 3-D points, sampling a good 

grasping point, and using a heuristic for 

the orientation and the direction of the 

hand. 

11:15–11:30 TuBT9.8 

Inverse Reinforcement Learning 

using Path Integrals 

Navid Aghasadeghi and Timothy W. Bretl 

University of Illinois at Urbana-Champaign, USA 

• Problem: Given an optimal trajectory 

generated by a continuous-time system, 

find a weights of parameterized cost 

function that would explain it 

• Use the Path Integral formulation to find 

probability over trajectories, 

parameterized � by 

P (� m | �,� ) � 

• Solution involves two parts: 

• Maximum likelihood to find the weights of the 

parameterized cost function 

• Iteratively update the set of sampled 

trajectories, by solving a forward optimal 

control with respect to the current cost estimate 


–74– 

K 

� 

1 *T 

� � � (� m ) 

� e 

k �1 

e 

1 *T 

� � � (� k ) 

� 

True cost and optimal trajectory 

Estimate cost and trajectory

Session TuCT1 Continental Parlor 1 Tuesday, September 27, 2011, 14:00–15:30 

Object Detection & Collision Avoidance 

Chair Paolo Zani, Univ. of Parma, Italy 

Co-Chair Jyh-Ming Lien, George Mason Univ. 

14:00–14:15 TuCT1.1 

Real-time Bézier Trajectory Deformation for 

Potential Fields Planning Methods 

L.Hilario * , N. Montés * , M.C.Mora ‡ , A.Falcó* 

* Physics, Mathematics and Computation Sciences Dept.,Cardenal Herrera 

University CEU, Spain 

‡ Mechanical Engineering and Construction Dept., Jaume I Universtity, Spain 

• A novel technique for obtaining trajectories 

based on the deformation of Bézier curves 

(BTD) is presented. 

• The deformation is computed through 

vectors. These are obtained with any 

Artificial Potential field methods. 

• In the present paper, the BTD has been 

combined with the Potential Field 

Projection method (PFP). 

• Next image shows a PFP-BTD example in 

an environment with 5 mobile obstacles. 

14:30–14:45 TuCT1.3 

Positive and Negative Obstacle Detection 

using the HLD Classifier 

Ryan D. Morton and Edwin Olson 

Computer Science & Engineering, University of Michigan, USA 

• New Terrain Classifier 

• Detects both positive and negative 

obstacles 

• Handles partial observability 

• Gives designers ability to manage 

classification risk 

• Subsumes and outperforms previous 

methods 

14:15–14:30 TuCT1.2 

Fast and Robust 2D Minkowski Sum 

Using Reduced Convolution 

Evan Behar and Jyh-Ming Lien 

Department of Computer Science, George Mason University, USA 

� M-sum is used for many tasks, such as 

C-space mapping and motion planning 

� Convolution of P, Q is sum of P's edges 

with Q's vertices, and vice versa 

� Prevailing convolution-based M-sum 

method computes the arrangement of full 

convolution, and then throws most of it 

away 

� Our method uses a reduced 

convolution and fast filters to avoid this 

wasted computation 

� It is shown experimentally to be faster 

than the full convolution method used by 

CGAL 


–75– 

An example reduced convolution 

generated from a neuron with 

holes and a circle 

14:45–14:50 TuCT1.4 

Real-time Swept Volume and Distance 

Computation for Self Collision Detection 

Holger Täubig 

Safe & Secure Cognitive Systems, DFKI, Germany 

Berthold Bäuml 

DLR Institute of Robotics and Mechatronics, Germany 

Udo Frese 

Safe & Secure Cognitive Systems, DFKI, Germany 

• Continuous collision detection algorithm 

for industrial and humanoid robots 

• Computes swept volumes of all bodies 

and checks them pairwise for collisions – 

real-time (0.4ms) 

• Input: joint angle intervals – cover whole 

movement 

• Volume representation: sphere swept 

convex hulls – tight, efficient, numerically 

stable 

• Kinematic tree: operation set models 

different joints – allows for a trade-off 

between accuracy and computation time


Object Detection & Collision Avoidance 

Chair Paolo Zani, Univ. of Parma, Italy 

Co-Chair Jyh-Ming Lien, George Mason Univ. 

14:50–14:55 TuCT1.5 

Visual Navigation With Obstacle Avoidance 

Andrea Cherubini and François Chaumette 

INRIA Rennes – Bretagne Atlantique 

• Visual navigation with 

obstacle avoidance is 

achieved 

• Obstacles are detected by a 

range scanner and modeled 

with potential fields 

• Collision avoidance and 

visual navigation are 

achieved concurrently by 

actuating the camera pan 

angle 

• The approach is validated in 

a series of real outdoor 

experiments 

15:00–15:15 TuCT1.7 

Clustering Obstacle Predictions to Improve 

Contingency Planning for Autonomous Road 

Vehicles in Congested Environments 

Jason Hardy and Mark Campbell 

Department of Mechanical and Aerospace Engineering, 

Cornell University, United States 

• Hierarchical clustering algorithm 

for improving the computational 

scaling of contingency planning 

• Contingency planning correctly 

handles mutually exclusive 

obstacle trajectory predictions 

• Increase the utility of the 

available contingency plans by 

maximizing dissimilarity between 

obstacle prediction clusters 

• Goal: capture the most important 

mutually exclusive obstacle 

decisions and uncertainties with a 

limited set of contingency clusters 

Application: autonomous road 

vehicles in congested environments 

14:55–15:00 TuCT1.6 

Stereo obstacle detection in challenging 

environments: the VIAC experience 

Alberto Broggi and Michele Buzzoni 

and Mirko Felisa and Paolo Zani 

VisLab, University of Parma, Italy 

• Stereo-based 3D world mapping using a 

parallel SGM approach 

• Obstacles points are clustered according 

to a spatial compatibility criterion 

• Very general approach, working reliably in 

a wide variety of scenarios 

• Tested on an intercontinental route from 

Parma, Italy, to Shanghai, China. 


–76– 

(a) 

(b) 

(a) The test vehicle, and (b) the 

obstacle detection algorithm 

output: color encodes disparity 

15:15–15:30 TuCT1.8 

Time Parametrization of Prioritized Inverse 

Kinematics Based on Terminal Attractors 

Gerardo Jarquín, Gustavo Arechavaleta and Vicente Parra-Vega 

Robotics and Advanced Manufacturing Group, CINVESTAV-IPN, México 

• Terminal attractors are incorporated 

to the task-priority redundancy 

formalism. 

• Each prioritized reaching task is 

achieved according to a desired time. 

• The strategy guarantees smooth 

transitions between active and 

inactive tasks.


Motion Estimation, Mapping & SLAM 

Chair Ryan Eustice, Univ. of Michigan 

Co-Chair Davide Scaramuzza, Univ. of Pennsylvania 

14:00–14:15 TuCT2.1 

Stereo Depth Map Fusion 

for Robot Navigation 

Christian Häne 1 , Christopher Zach 1 , Jongwoo Lim 2 , 

Ananth Ranganathan 2 and Marc Pollefeys 1 

Department of Computer Science, ETH Zürich, Switzerland 1 

Honda Research Institute, Mountain View, CA, USA 2 

• Fusion of range data from stereo image 

pairs to a representation suitable for robot 

navigation 

• Reconstruction restricted to a two level 

height map representation with a floor and 

a ceiling for efficient processing 

• Regularization of height levels with a 

convex total variation energy functional to 

get globally optimal solutions 

Top: Left image from one input stereo pair 

Middle: Raw depth map from stereo matching 

Bottom: Final regularized two level reconstruction 

14:30–14:45 TuCT2.3 

Building Facade Detection, Segmentation, and 

Parameter Estimation for Mobile Robot 

Localization and Guidance 

Jeffrey A. Delmerico and Jason J. Corso 

Dept. of Computer Science and Engineering, SUNY at Buffalo, USA 

Philip David 

US Army Research Laboratory, USA 

• Method for segmenting and modeling building facades in stereo imagery. 

• Proposed application in mobile robotics is localization in urban 

environments by registration of façade orientations to aerial images. 

• 85% accuracy for segmentation into individual facades; 10� accuracy for 

modeling planar façade orientations. 

• New benchmark dataset – human-annotated stereo images of buildings. 

14:15–14:30 TuCT2.2 

An Embedded Stereo Vision Module 

for 6D Pose Estimation and Mapping 

Giacomo Spampinato, Jörgen Lidholm, Carl Ahlberg, 

Fredrik Ekstrand, Michael Ekström and Lars Asplund 

School of Innovation Design and Engineering, 

Mälardalen University, Sweden 

• The paper presents an embedded stereo 

vision system based on FPGA to perform 

6D vSLAM 

• The proposed architecture is both energy 

and cost efficient with high versatility 

• A real-time sparse visual feature detector 

is used as the only source of information 

• Experiments on small and large scale in 

non flat scenarios are presented 


–77– 

Large scale visual SLAM in 

industrial scenario 

14:45–14:50 TuCT2.4 

A Strategy for Efficient Observation Pruning in 

Multi-Objective 3D SLAM 

Jaime Valls Miro, Weizhen Zhou and Gamini Dissanayake 

Centre of Excellence for Autonomous Systems, 

Faculty of Engineering and IT, 

University of Technology Sydney (UTS), 

Australia 

• Efficient solution to feature-based 3D 

SLAM when competing objectives 

operate simultaneously 

• Formulation automatically exploits 

observations to best fulfil differing 

objectives: localisation, mapping, 

exploration, feature distribution, victim 

search, etc. 

• Tested in Search and Rescue scenario: 

21% of observations processed in 1/3 of 

time required for full SLAM, with little 

degradation in map size and quality 

2D projection of spatial 

coverage/map expansion (in a 

USAR environment), one of the 

competing objectives assessed 

by the proposed dynamic SLAM 

strategy


Motion Estimation, Mapping & SLAM 

Chair Ryan Eustice, Univ. of Michigan 

Co-Chair Davide Scaramuzza, Univ. of Pennsylvania 

14:50–14:55 TuCT2.5 

Combined Visually and Geometrically 

Informative Link Hypothesis for Pose-graph 

Visual SLAM using Bag-of-Words 

Ayoung Kim* and Ryan Eustice † 

*Department of Mechanical Engineering 

† Department of Naval Architecture & Marine Engineering 

University of Michigan, USA 

• Expected information gain combined with 

visual saliency score is reported. 

• Geometrically and visually informative 

loop-closure candidates are selected. 

• Two different bag-of-words saliency 

metrics are introduced – global saliency 

and local saliency. 

• Global saliency measures the rarity. 

• Local saliency describes texture richness 

and usefulness for camera measurement. 

• Results with both indoor and underwater 

imagery are presented. 

15:00–15:15 TuCT2.7 

3D Surveillance Coverage Using Maps Extracted 

by a Monocular SLAM Algorithm 

Lefteris Doitsidis Alessandro Stephan Weiss 

Department of 

Electronics, TEI Crete & 

CERTH/ITI, Greece 

Renzaglia 

INRIA,France 

ETHZ, Switzerland 

Elias Kosmatopoulos Davide Scaramuzza Roland Siegwart 

Dept. of ECE, DUTH & University of 

ETHZ, Switzerland 

CERTH/ITI, Greece Pennsylvania, Greece 

• Surveillance Coverage over a terrain of 

arbitrary morphology. 

• Two-step centralized procedure for 

optimal alignment of the robot team. 

• A single robot constructs the map using a 

novel monocular-vision-based approach. 

• The optimal arrangement of the robot 

team is produced using a Cognitive based 

optimization approach and maximizes the 

monitored area. 

14:55–15:00 TuCT2.6 

RS-SLAM: RANSAC Sampling for Visual SLAM 

Gim Hee Lee, Friedrich Fraundorfer, and Marc Pollefeys 

Computer Vision and Geometry Laboratory, Department of Computer Science, 

ETH Zürich, Switzerland 

• RS-SLAM is a monocular FastSLAM 

framework that uses hypotheses from 

5-point RANSAC and image feature 

uncertainties as proposal distribution 

• Our proposal distribution is more 

robust than the commonly used 

constant velocity model which could 

be easily violated 

• We also showed results from the 

extension of our RS-SLAM for stereo 

camera 


–78– 

(Top) Particles trajectories during loop closure 

(Bottom) 3D map generated from the particle 

with the highest weight 

15:15–15:30 TuCT2.8 

Adaptive Sampling Using Mobile Robotic 

Sensors 

Shuo Huang and Jindong Tan 

Electrical & Computering Engineering 

Michigan Technological University, USA 

• An adaptive sparse sampling approach 

based on mobile robotic sensors 

• Unknown sensing fields modeled as 

sparse signals under Haar Wavelet 

domain 

• Measurements determined and collected 

considering possible distribution of target 

sparse signals 

• Sensing information maximized with 

number of measurements substantially 

reduced 

Great Lakes surface temperature 

sensing and reconstruction


Symposium: Microrobotics III 



14:00–14:15 TuCT3.1* 

Semi-Plenary Invited Talk: Artificial, Biomimetic and 

Biological Microrobotics 

Metin Sitti, Carnegie Mellon University 

14:30–14:45 TuCT3.3 

Chemotactic Behavior and Dynamics of 

Bacteria Propelled Microbeads 

Dongwook Kim, Albert Liu, and Metin Sitti 


• Proposed chemotaxis based steering 

control of swimming micro-robotic 

bodies propelled by attached 

flagellated S. marcescens bacteria 

• Proposed a method to create 

chemoattactant gradient in enclosed 

fluidic chambers using a mixture 

consisting of agar and 

chemoattractants 

• Demonstrated that chemical 

attractant directs the stochastic 

motion of bacteria propelled beads, 

which is promising as a steering 

control method 

Bead trajectories when there is 

no chemical attractant in the medium 

Bead trajectories when there is 

chemical gradient in the south direction 

14:15–14:30 TuCT3.2 

Semi-Plenary Invited Talk: Artificial, Biomimetic and 

Biological Microrobotics 

Metin Sitti, Carnegie Mellon University 

14:45–14:50 TuCT3.4 

First Leaps Toward Jumping Microrobots 

Wayne A. Churaman 1,3 , Aaron P. Gerratt 1 , and 

Sarah Bergbreiter 1,2 

1 Mechanical Engineering, University of Maryland, College Park, USA 

2 Institute for Systems Research, University of Maryland, College Park, USA 

3 Army Research Lab, USA 

• Two jumping microrobots demonstrated 

using both stored mechanical energy and 

stored chemical energy to provide thrust 

• Robot using stored mechanical energy is 

8 mg, 4 x 4 x 0.3 mm 3 and has 

demonstrated jump heights of 32 cm 

when loaded and released with tweezers 

• Robot using stored chemical energy is 

300 mg and 4 x 7 x 4 mm 3 but also 

includes power, sensing, and control on 

board in addition to chemical actuators – 

autonomous jumps 8 cm high 

demonstrated in response to light stimulus 


–79– 

Two jumping microrobots using 

(a) stored mechanical energy and 

(b) stored chemical energy for 

jump


Symposium: Microrobotics III 



14:50–14:55 TuCT3.5 

Micro-Scale Propulsion using 

Multiple Flexible Artificial Flagella 

John Singleton, Eric Diller, Tim Andersen, Metin Sitti 

Mechanical Engineering, Carnegie Mellon University, USA 

Stéphane Regnier 

Institute of Intelligent Systems and Robotics 

Univ. Pierre et Marie Curie, France 

• Multiple artificial flagella are used 

to increase propulsion of microscale 

swimmers 

• For simplicity of actuation and 

fabrication, all flagella rotate 

about common axis 

• Stiff and flexible helices and rods 

are tested and analyzed, 

suggesting increase in thrust 

over single flagella 

Concept sketch of the proposed 

micro-scale propulsion system 

with multiple flexible flagella 

protruding downwards from the 

perimeter of the cylindrical body 

15:00–15:15 TuCT3.7 

Precision Evaluation of Modular Multiscale 

Robots for Peg-in-Hole Microassembly Tasks 

Aditya N. Das and Dan O. Popa 

The University of Texas at Arlington, USA 

• Peg-in-hole microassembly is a generic 

problem, with yield dependent on both 

part tolerance and robot precision 

• We present simulation results evaluating 

the precision of several modular robotic 

configurations 

• Simulations are based on explicit 

uncertainty models and Monte-Carlo runs 

• Results confirm intuition that most precise 

robot design include rotation DOFs at the 

end of two manipulator chains 

Rotational 

stages 

Angle adapter 

(c) 

Translational stages 

Stages 

End-effector 

Final pose 

Initial pose 

Evaluation of peg-in-hole 

microassembly yield 

with modular robotic stages 

14:55–15:00 TuCT3.6 

Manipulation of 

Multiple Bacteria-driven Microobjects 

Based on Bacterial Autonomous Movement 

Kousuke Nogawa 1 , Masaru Kojima 1 , Masahiro Nakajima 1 , 

Michio Homma 2 , Fumihito Arai and Toshio Fukuda 1 

1 Institute for Advanced Research, Nagoya University, Japan 

2 Department of Micro-Nano Systems Engineering, Nagoya University, Japan 

3 Center For Micro-nano Mechatronics, Nagoya University, Japan 

4 Division of Biological Science, Nagoya University, Japan 

• We propose a method of 

smart manipulation of 

multiple bacteria-driven 

microobjects with single 

manipulated guiderobot 

based on bacterial 

autonomous movement, 

named “SMARTBOT”. 

• Fundamental capability of 

SMARTBOT is verified by 

using the micro/nano pipettes 

instead of the guiderobot. 


–80– 

Signal 

Target 

source 

Global control methods 

Blood 

vessels 

Microrobots 

Bacteria-driven 

microrobots 

Fully manipulated multi-argents Fully autonomous by bacterial function 

Bacteria-driven 

microrobots 

Guide robot 

Smart manipulation of multiple agent with single manipulated guide robot 

Autonomous mass manipulation of 

bacteria- driven microobjects with 

single manipulated guide robot 

15:15–15:30 TuCT3.8 

Multipoint Sliding Probe Methods for In situ 

Electrical Transport Property Characterization of 

Individual Nanostructures 

Zheng Fan 1 , Xinyong Tao 2 , Xiaodong Li 3 , and Lixin Dong 1 

1 Dept. of Elec. & Comput. Engr., Michigan State University, USA 

2 College of Chem. Engr. & Mater. Science, Zhejiang Univ. of Tech., China 

3 Dept. of Mech. Engr., University of South Carolina, USA 

• Sliding probe methods are 

designed for the in situ electrical 

characterization of individual 1D 

nanostructures. 

• Contact force is controlled with a 

Cu-nanowire-tipped flexible probe. 

• Specimen-shape adapting probe is 

developed for keeping a constant 

contact area. 

• The technique has a higher 

resolution and simplicity in setup 

as compared with conventional 

two- and four-terminal methods, 

respectively. 

Sliding Probe Methods and TEM 

Images of Measurements

Session TuCT4 Continental Ballroom 4 Tuesday, September 27, 2011, 14:00–15:30 

Forum: Robotics: Beyond the Horizon 

Chair Hirochika Inoue, AIST 

Co-Chair 


–81–


Symposium: Medical Robotics III 



14:00–14:15 TuCT5.1 

Spatial and Temporal Movement Characteristics 

after Robotic Training of Arm and Hand: 

A Case Study of a Person with 

Incomplete Spinal Cord Injury 

D.P. Eng, Dept of MEMS, Rice University, USA 

Z. Kadivar, Dept of PM&R, Baylor College of Medicine,USA 

J.L. Sullivan, A.U. Pehlivan, M.K. O’Malley 

Dept of MEMS, Rice University, USA 

N. Yozbatiran and G.E. Francisco, Dept of PM&R, UTHSC, USA 

• Application of RiceWrist robotic device for 

upper-limbs of a patient with spinal cord 

injury 

• Spatial and temporal movement aspects 

were determined before and after the 

training 

• Greater improvements were observed for 

temporal aspects of forearm and wrist 

movements 

14:30–14:45 TuCT5.3 

Simulating Prosthetic Devices with 

Human-Inspired Hybrid Control 

Ryan W. Sinnet, Huihua Zhao, and Aaron D. Ames 

Department of Mechanical Engineering, Texas A&M University, USA 

• A human walking experiment provides 

data from test subjects. Data from four 

of the subjects are considered to 

strengthen the result. 

• Human functions are designed which 

represent the most fundamental 

behaviors underlying human walking. 

• A simulation environment is described 

which attempts to reproduce human 

walking using human functions on a 

model with humanlike measurements. 

• A transfemoral prosthesis is substituted 

for one of the legs and simulation 

results in stable walking; see figure. 

Hybrid model of prosthesis (top), 

healthy gait with human-inspired 

control (middle), and prosthesis gait 

(bottom) 

14:15–14:30 TuCT5.2 

Robotic Rehabilitation System Using Human 

Hand Trajectory Generation Model 

in Virtual Curling Task 

Yoshiyuki Tanaka, Toru Sanemasa, Toshio Tsuji 

Graduate School of Engineering, Hiroshima University, Japan 

Nobuaki Imamura 

Department of Engineering, Hiroshima Kokusai University, Japan 

• This paper discusses the training 

methodology using hand trajectory 

generation model in virtual curling. 

• The hand motion by well-trained subject is 

expressed within the framework of the 

minimum-jerk model. 

• The model is utilized for teaching and 

assisting trainee's motion according to 

individual motor abilities. 

• Effectiveness of the proposed training 

approach was validated through 

preliminary training experiments with the 

beginners. 


–82– 

Velocity, vy [m/s] 

0.45 

0.3 

0.15 

0 

House 

Release Release 

line 

Stone Stone 

Distance, l [m] 

Reference motion 

A minimum-jerk model 

with task-related constraints 

-0.15 

4.0 

3.5 

1.0 

3.0 

0 

-1.0 

2.5 

-2.0 [s] 

2.0 

-3.0 Time 

14:45–14:50 TuCT5.4 

Dual Predictive Control of Electrically Stimulated 

Muscle using Biofeedback for Drop Foot Correction 

Mitsuhiro Hayashibe, Qin Zhang, Christine Azevedo-Coste 

DEMAR INRIA Sophia Antipolis, and LIRMM, France 

• Development of an ES control strategy for 

drop foot correction with torque tracking 

performance. 

• ES-evoked EMG signal and joint torque 

were recorded for the model identification 

with Kalman filter with forgetting factor. 

• A dual predictive controller was proposed 

for explicit consideration of muscle activity. 

• The method was verified by generating 

control signal adaptively according to the 

current muscle condition.


Symposium: Medical Robotics III 



14:50–14:55 TuCT5.5 

Gait Support for Complete Spinal Cord Injury 

Patient by Synchronized Leg-Swing with HAL 

A. Tsukahara, Y. Hasegawa and Y. Sankai 

Center for Cybenics Research, University of Tsukuba, Japan 

• This paper proposes an estimation 

algorithm that infers the intention related 

to the forward leg-swing 

- Algorithm: Gait-intention estimator 

• The system including the algorithm infers 

the intention in synchronization with the 

center of ground reaction force 

- System: Hybrid Assistive Limb (HAL) 

• The effectiveness of the proposed 

algorithm was verified through a clinical 

trial 

- Clinical trial: 10m walking test on a treadmill 

Complete spinal cord injury 

patient wearing HAL 

15:00–15:15 TuCT5.7 

Knee Joint Movement Assistance Through 

Robust Control of an Actuated Orthosis 

Saber Mefoued, Samer Mohammed, and Yacine Amirat 

University of Paris Est Creteil - (UPEC) 

LISSI Laboratory, 94400 

Vitry Sur Seine, France 

• Design of an actuated orthosis intended to 

restore lower limb movements of dependent 

persons. 

• Dynamic modeling and parametric 

identification of the biomechanical – orthosis 

model. 

• Experimental implementation of a High 

Order Sliding Mode Controller (HOSMC). 

• Evaluation of controllers’ performances in 

terms of tracking errors and robustness 

against external perturbations. 

Actuated knee joint orthosis 

14:55–15:00 TuCT5.6 

Subject-based Motion Generation Model 

with Adjustable Walking Pattern 

for a Gait Robotic Trainer: NaTUre-gaits 

Ping Wang and K. H. Low 

School of Mechanical and Aerospace Engineering 

Nanyang Technological University (NTU), Singapore 

A. H. McGregor 

Charing Cross Hospital, Imperial College London, UK 

• The exoskeleton modules are mounted on 

the mobile platform and attached to the 

lower limbs and pelvis in parallel 

• The synchronized motion generation for 

the exoskeleton modules is provided by 

virtue of the inverse kinematic model 

analysis 

• The pelvic trajectory is predefined and ten 

points are specified within one gait cycle 

to obtain the foot trajectory from the 

designated step length and height 

• The trajectory is obtained via curve fitting 

based on these specified points 

• On the other hand, in order to keep the 

desired foot trajectory, the pelvic motion is 

compensated to accommodate hip and 

knee joint angles 


–83– 


Ten points definition as seen 

from fixed external observer 

15:15–15:30 TuCT5.8 

Stairs-Ascending/Descending Assist for a Lower- 

Limb Power-Assist Robot Considering ZMP 

Yoshiaki Hayashi and Kazuo Kiguchi 

Dept. of Advanced Technology Fusion, Saga University, Japan 

• The perception-assist for a lower-limb power-assist robot 

has been studied. 

• In this paper, the stairs-ascending/descending assist is discussed 

to prevent the user from falling down. 

• In the proposed method, the perception-assist is carried out 

considering ZMP. 

• The effectiveness of the proposed method has been evaluated 

by the performing experiments. 

Lower-limb power-assist 

exoskeleton robot.


Symposium: Grasping and Manipulation: Grasp Planning and Quality 

Chair Jeff Trinkle, Rensselaer Pol. Inst. 

Co-Chair Elon Rimon, Tech. - Israel Inst. of Tech. 

14:00–14:15 TuCT6.1* 

Semi-Plenary Invited Talk: Sensorless Grasping with 

Multiple Contacts and its Computational Modeling 

Suguru Arimoto, Ritsumeikan University 

14:30–14:45 TuCT6.3 

Graspability Map: 

A Tool for Evaluating Grasp Capabilities 

Maximo A. Roa, Katharina Hertkorn, Franziska Zacharias, 

Christoph Borst and Gerd Hirzinger 

Institute of Robotics and Mechatronics, German Aerospace Center, Germany 

Graspability maps for a coffee cup with the DLR hand II and the DLR-HIT hand II 

• Novel representation of the positions and orientations that allow force 

closure precision grasps for a given object and mechanical hand 

• The maps allow the comparison of grasp capabilities for different 

mechanical hands 

• Potential applications of the maps include autonomous grasp and 

manipulation planning 

14:15–14:30 TuCT6.2 

The OpenGRASP Benchmarking Suite: 

An Environment for the Comparative Analysis of 

Grasping and Dexterous Manipulation 

Stefan Ulbrich, Daniel Kappler, Tamim Asfour, 

Nikolaus Vahrenkamp, Alexander Bierbaum, Markus Przybylski 

and Rüdiger Dillmann 

Institute for Anthropomatics, Karlsruhe Institute of Technology, Germany 

• A new software environment for 

comparative evaluation of algorithms for 

grasping and dexterous manipulation. 

• Allows the reproduction of well-defined 

experiments in real-life scenarios in every 

laboratory. 

• Extendable structure in order to include a 

wider range of benchmarks. 

• Real-life scenario featuring a humanoid 

robot acting in a kitchen with domestic 

everyday objects. 


–84– 

Screenshot of the 

real-life scenario. 

14:45–14:50 TuCT6.4 

Experimental evaluation of Postural Synergies 

during Reach to Grasp with the UB Hand IV 

Fanny Ficuciello1 , Gianluca Palli2 , Claudio Melchiorri2 and Bruno Siciliano1 1Dipartimento di Informatica e Sistemistica, 

Università degli studi di Napoli Federico II, Italy 

2Dipartimento di Elettronica, Informatica e Sistemistica, 

Università di Bologna, Italy 

• The Postural Synergies configuration 

subspace given by the fundamental 

eigengrasps of the UB Hand IV is derived 

through experiments. 

• The study is based on the kinematic 

structure of the hand and on the taxonomy 

of the grasps of common objects. 

• Experimental results show that grasp 

synthesis for precision or power grasps is 

obtained by using only the first two 

eigengrasps. 

• The tasks are planned for reach and grasp 

phases, unique for each object/grasp 

combination, by suitable tuning the 

eigenpostures weight. 

Power and Precise Grasp of 

Prismatic and Circular objects 

obtained using Postural 

Synergies


Symposium: Grasping and Manipulation: Grasp Planning and Quality 

Chair Jeff Trinkle, Rensselaer Pol. Inst. 

Co-Chair Elon Rimon, Tech. - Israel Inst. of Tech. 

14:50–14:55 TuCT6.5 

Planning Grasps for Robotic Hands using a Novel 

Object Representation based on the Medial Axis 

Transform 

Markus Przybylski, Tamim Asfour and Rüdiger Dillmann 

KIT – Karlsruhe Institute of Technology, Germany 

• We present the novel grid of medial 

spheres object representation. 

• The grid of medial spheres contains 

symmetry properties of an object. 

• We present a grasp planning algorithm 

that operates on the grid of medial 

spheres. 

• We show experimental results with two 

robot hands on two sets of object models. 

15:00–15:15 TuCT6.7 

Prioritized Independent Contact Regions for 

Form Closure Grasps 

Robert Krug, Dimitar Dimitrov, Krzysztof Charusta and Boyko Iliev 

AASS Research Center, Oerebro University, Sweden 

• Independent Contact Regions (ICR) 

provide robustness to grasp uncertainty 

• We propose an Algorithm allowing for 

user-input regarding the shape of such 

regions 

• An extension of the Grasp Wrench Space 

(GWS) to sets of grasps is contributed 

• We introduce the concept of the Exertable 

Wrench Space (EWS) to evaluate grasp 

families 

14:55–15:00 TuCT6.6 

Exploiting potential energy storage for cyclic 

manipulation: An analysis for elastic dribbling 

with an anthropomorphic robot 

S. Haddadin, K. Krieger, M. Kunze, A. Albu-Schäffer 

Institute of Robotics and Mechatronics (DLR), Germany 

• Stable model based ball dribbling by 

proprioceptive feedback only, no vision 

feedback 

• Utilizing intrinsically compliant fingers for 

protecting the robot and enlarging contact 

time 

• Exploiting dynamic elastic energy storage 

and release for better peak power 

performance and robustness 

• Full 6DoF dribbling experiments with a 

Cartesian impedance controlled 7DoF 

anthropomorphic LWR 


–85– 

Elastic dribbling: from modeling to experiment 

15:15–15:30 TuCT6.8 

Abort and Retry in Grasping 

Alberto Rodriguez, Matthew T. Mason, Siddhartha S. Srinivasa 

Matthew Bernstein, and Alex Zirbel 


• Early abort and retry in a bin-picking task 

• The system learns a probabilistic model of 

the instantaneous likelihood of success of 

the grasp 

Successful grasp Failed grasp 

• Markov chain model of the iterative process 

of abort and retry is used to optimize the 

expected time to completion of a successful 

grasp 

• Experiments with a simple hand show 

significant increase in time efficiency 

Bin picking scenario


Mechanisms: Actuator Design 

Chair I-Ming Chen, Nanyang Tech. Univ. 

Co-Chair Ravi Balasubramanian, Yale Univ. 

14:00–14:15 TuCT7.1 

Stiffness Adjustment of a Series Elastic Actuator 

in a Knee Prosthesis for Walking and Running: 

The Trade-off between Energy and Peak Power Optimization 

Martin Grimmer and André Seyfarth 

Lauflabor Locomotion Laboratory, University of Jena, Germany 

Modelling on human experimental data shows: 

• a SEA can highly reduce peak power and 

energy for running and high speed walking 

• for preferred walking speeds only a energy 

reduction is possible 

• a benefit with constant stiffness values for 

running and walking at different speeds is 

possible 

Concept of an active knee prosthesis 

used to estimate peak power and 

energy requirements with a SEA in 

comparison to a direct drive 

14:30–14:45 TuCT7.3 

Performance of Serial Underactuated Mechanisms: 

Number of Degrees of Freedom and Actuators 

Ravi Balasubramanian and Aaron M. Dollar 


• Most underactuated mechanisms utilize 

only one actuator to actuate many 

degrees of freedom in a serial chain. 

• If additional actuators are to be used, what 

is the additional benefit that each actuator 

provides? 

• Metric used in paper: the number of 

contacts the mechanism makes with 

the environment. 

• What is the optimal way to route the 

additional actuators across the degrees of 

freedom? 

A three degree-of-freedom linear 

underactuated system a) with one 

actuator, and b) with two actuators. c) 

A schematic of the mechanism 

interacting with the environment. 

14:15–14:30 TuCT7.2 

Static and Dynamic Characteristics of 

McKibben Pneumatic Actuator for Realization 

of Stable Robot Motion 

Yasuhiro Sugimoto and Koichi Osuka 

Dept. of Mechanical Engineering, Osaka University, Japan 

Keisuke Naniwa 

Dept. of Mechanical Engineering, Kobe University, Japan 

• McKibben Pneumatic Actuator (MPA) has 

favorable properties on the stability of robot’s 

motion 

• The dependency of MPA tension-velocity 

through dynamic experiments was measured 

• It was verified that the static and dynamic 

MPA’s properties contribute to stable robot 

motions 


–86– 

Relation between MPA tension and 

contraction velocity at closed valve 

14:45–14:50 TuCT7.4 

Variable Radius Pulley Design Methodology for 

Pneumatic Artificial Muscle-based Antagonistic 

Actuation Systems 

Dongjun Shin1 , Xiyang Yeh2 , Oussama Khatib1 1 

Artificial Intelligence Laboratory, Stanford University, USA 

2 

Mechanical Engineering , Stanford University, USA 

• Analysis of the limitations of 

conventional circular pulleys 

• A new design methodology for 

variable radius pulleys 

• Experimental comparisons to 

show performance 

improvement in position control 

in the enlarged workspace. 

Variable Radius Pulleys in Pneumatic 

Muscle-driven 1-DOF Testbed


Mechanisms: Actuator Design 

Chair I-Ming Chen, Nanyang Tech. Univ. 

Co-Chair Ravi Balasubramanian, Yale Univ. 

14:50–14:55 TuCT7.5 

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15:00–15:15 TuCT7.7 

Kinematic and Dynamic Analysis of a 

Novel 6-DOF Serial Manipulator for 

Underground Distribution Power Lines 

J.-F. Allan, S. Lavoie, S. Reiher, and G. Lambert 

Hydro-Quebec’s Research Institute (IREQ), Canada 

• New manipulator with five revolute joints 

and one prismatic joint 

• Manipulator architecture is intended to 

solve space constraint problems in 

confined environments 

• Presentation of the geometrical model, 

analytical solution for the inverse 

kinematic equations, and dynamic model 

• Simulations performed with MATLAB and 

CATIA, manipulator workspace 

• The only robot application in the world 

designed to perform operation tasks on 

equipment in vaults of an underground 

power distribution network 

Manipulator design in CATIA 

14:55–15:00 TuCT7.6 

High-Backdrivable Parallel-Link Manipulator 

with Continuously Variable Transmission 

Kenji Tahara, Shingo Iwasa, Shu Naba and Motoji Yamamoto 

Kyushu University, Japan 

• A novel parallel-link manipulator 

composed of several linear shaft motors is 

proposed. 

• A continuously variable transmission 

(CVT) mechanism is utilized. 

• Kinematics and dynamics of the 1 DOF 

parallel-link manipulator are modeled 

• A control signal to regulate the arm's 

angle and the CVT machanism is 

designed. 

• A static relation between the output force 

and the CVT mechanism is analyzed. 

• Experiments are conducted using a 

prototype. 


–87– 

Effect of the continuously variable 

transmission mechanism 

15:15–15:30 TuCT7.8 

Design of a Static Balancing Mechanism with 

Unit Gravity Compensators 

Changhyun Cho 

Dept. of Control, Instruments, and Robot, Chosun University 

Sungchul Kang 

Center for Bionics, KIST 

• Design method of a static balancing 

mechanism using unit gravity 

compensators (e.g., 1-dof gravity 

compensator) 

• Determination of the number of springs 

and their locations by computing the 

mapping matrix between the joint space 

and gravity compensator space. 

• The number of rows of the mapping matrix 

indicates the amount of unit gravity 

compensators and linear joint constraints 

representing locations of unit gravity 

compensators 

� 

Joint 

space 

J� = �g 

J 

J 

Gravity 

Compensator 

space 

-1 �g 

Mapping between the joint space 

and gravity compensator space


Reptile & Fish-inspired Robots 

Chair Akio Ishiguro, Tohoku Univ. 

Co-Chair Fumitoshi Matsuno, Kyoto Univ. 

14:00–14:15 TuCT8.1 

Learning Fish-like Swimming with a CPG-based 

Locomotion Controller 

Y. Hu, W. Tian, J. Liang, and T. Wang 

Beihang University, China 

• Fish-like swimming can be acquired with 

an adaptive CPG network 

• Teaching signals are derived from fish 

kinematics with trajectory approximation 

method 

• A coupling scheme that eliminates the 

influence of afferent signals on amplitude 

of the oscillator is proposed 

• CPG parameters are converted into 

dynamical systems that evolve as part of 

the CPG network 

14:30–14:45 TuCT8.3 

Decentralized Control of Multi-articular 

Snake-like Robot for Efficient Locomotion 

Takeshi Kano 1 , Takahide Sato 1 , 

Ryo Kobayashi 2,3 , and Akio Ishiguro 1,3 

1 Research Institute of Electrical Communication, Tohoku University, Japan 

2 Dept. of Mathematical and Life Sciences, Hiroshima University, Japan 

3 Japan Science and Technology Agency, CREST, Japan 

• We theoretically show that multiarticular 

muscles of snakes 

enhance locomotion efficiency 

• We propose a decentralized control 

scheme for efficient locomotion of a 

multi-articular snake-like robot on 

the basis of the theoretical result 

• The validity of the proposed control 

scheme is confirmed by simulations 

n � 1 

n � 2 

n � 3 

n � 4 

n � 5 

Direction of motion 

14:15–14:30 TuCT8.2 

A Self-tuning Multi-phase CPG Enabling the 

Snake Robot to Adapt to Environments 

Chaoquan Tang 1,2 , Shugen Ma 2 , Bin Li 1 , Yuechao Wang 1 

1, State Key Laboratory of Robotics, Automation, 

Chinese Academy of Sciences, Shenyang, China 

2, Department of Robotics, College of Science and Engineering, 

Ritsumeikan University, Shiga-ken, Japan 

• A control method imitates the control 

strategy of natural snake’s movement in 

different environments, which enables 

the snake robot to move more quickly 

and naturally. 

• Through kinematic and dynamic analysis 

of snake robots, optimal control 

parameters are chosen for the decision 

strategy. 

• Due to the intrinsic property of the multiphase 

CPG, this model can change the 

movement patterns and control 

parameters autonomously according to 

external information. 


–88– 

The comparison of adaptability 

between multi-phase CPG control 

method(b) and traditional control 

method(a). 

14:45–14:50 TuCT8.4 

A Snake-like Robot Driven by 

a Decentralized Control That Enables 

Both Phasic and Tonic Control 

Takahide Sato1 , Takeshi Kano1 , and Akio Ishiguro1,2 1Research Institute of Electrical Communication, Tohoku University, Japan 

2Science and Technology Agency CREST, Japan 

• We have developed two-dimensional 

snake-like robot employing a 

decentralized control scheme, in which 

phasic and tonic control are well 

coordinated. 

• A well-balanced coupling between the 

phasic and tonic control enables the robot 

to locomote on the upslope, which it 

cannot negotiate with only phasic control. 

Real physical serpentine 

robot developed


Reptile & Fish-inspired Robots 

Chair Akio Ishiguro, Tohoku Univ. 

Co-Chair Fumitoshi Matsuno, Kyoto Univ. 

14:50–14:55 TuCT8.5 

A Lizard-Inspired Active Tail Enables Rapid Maneuvers 

and Dynamic Stabilization in a Terrestrial Robot 

Evan Chang-Siu , Thomas Libby, Masayoshi Tomizuka 

Dept. of Mechanical Engineering, University of California Berkeley, USA 

Robert J. Full 

Dept. of Integrative Biology, University of California Berkeley, USA 

• Bio-inspiration from leaping lizards 

• 177g active tailed robot 

• Active tail allows redirection of angular 

momentum from body to tail 

• Active tail enables perturbation mitigation 

• Active tail enables steering of the body 

angle 

• High speed video demonstrating behavior 

Angle (deg) 

200 

100 

0 

0 0.05 0.1 0.15 0.2 

Time (sec) 

0.25 0.3 0.35 

Bio-Inspiration and Tailbot 

Tail Angle 

Body Angle 

15:00–15:15 TuCT8.7 

Multi-physics model of an electric fish-like robot: 

numerical aspects and 

application to obstacle avoidance 

Mathieu Porez*�, Vincent Lebastard�, 

Auke Jan Ijspeert* and Frédéric Boyer� 

*BioRob, EPFL, Switzerland - �IRCCyN, EMN, France 

• Modeling of a fish-like robot equipped with 

the electric sense suited to the sensorimotricity 

study 

• Study of interactions between body 

deformations and perception variables 

• Design and testing of an electric 

extroceptive feedback loop based on a 

direct current measurement method 

14:55–15:00 TuCT8.6 

Moving Right Arm in the Right Place: 

Ophiuroid-inspired Omnidirectional Robot 

Driven by Coupled Dynamical Systems 

Wataru Watanabe, Shota Suzuki and Takeshi Kano 

Research Institute of Electrical Communication, Tohoku University, Japan 

Akio Ishiguro 

Research Institute of Electrical Communication, Tohoku University, Japan, 

and Japan Science and Technology Agency CREST, Japan 

• Discuss a decentralized control that 

exhibits the coordination of 

rhythmic and non-rhythmic 

movements. 

• Focus on the ophiuroid 

omnidirectional locomotion as a 

simple best example. 

• Use an active rotator model that 

can describe both oscillatory and 

excitatory properties. 

• Simulated robot exhibits the 

spontaneous role assignment of 

versatile arm movements and 

generates omnidirectional locomotion. 


–89– 

Snapshots of the simulated 

locomotion of the ophiuroid robot. 

15:15–15:30 TuCT8.8 

Front-Unit-Following Control of a Snake-like 

Robot Using Screw Drive Mechanism Based on 

Past Velocity Commands 

Ryo Ariizumi, Hiroaki Fukushima and Fumitoshi Matsuno 

Dept. of Mechanical Engineering and Science, Graduate School of 

Engineering, Kyoto University, Japan 

• Present a front-unit-following control 

method for a snake-like robot using screw 

drive mechanism 

• Operators only have to command the first 

unit, then the other units automatically 

follow it 

• Proposed method estimates tracking 

errors expressed in Frenet frames using 

past velocity commands 

• Effectiveness of the method is 

demonstrated by computer simulations 

and laboratory experiments


Novel Sensors 

Chair Dario Floreano, Ec. Pol. Federal, Lausanne 

Co-Chair Sanford Meek, Univ. of Utah 

14:00–14:15 TuCT9.1 

Contactless deflection sensor for soft robots 

Michal Karol Dobrzynski, Ramon Pericet-Camara 

and Dario Floreano 

Laboratory of Intelligent Systems, 

Ecole Federale Polytechnique de Lausanne, Switzerland 

• Deflection sensor capable of shape 

estimation with no impact on 

deflected substrate’s softness. 

• Prototype devices show a resolution 

of 1.29� with 400 Hz data acquisition. 

• Average agreement of 5% between 

experimental data and simulation. 

• Application to soft robotics domain is 

discussed. 

Aa 

B 

Ab 

Aa: prototype device consist of 12 contactless 

deflection sensors; Ab: shape visualization; 

B: operational principle for a single contactless 

deflection sensor based on light. 

14:30–14:45 TuCT9.3 

Characterizing the performance of an optical slip 

sensor for grip control in a prosthesis 

Hamidreza N. Sani and Sanford G. Meek 


• Capable of detecting slip on surfaces with 

various surface proprieties such as 

roughness, curvature, transparency, 

reflectivity, and hardness 

• All tests were done with a 0.5 mm clear 

plastic to simulate the cosmetic glove 

• Accurate displacement detection up to 

1mm away 

• Only failed at extreme cases of surface 

properties 

• Minimal to no disturbance from an 

external light source even under the sun 

. The prototype optical based 

slip detection sensor mounted 

between the Motion Control 

prosthetic hand fingers 

14:15–14:30 TuCT9.2 

Soft Curvature Sensors for 

Joint Angle Proprioception 

Rebecca K. Kramer, Carmel Majidi, 

Ranjana Sahai and Robert J. Wood 


• Elastomer-based curvature 

sensors allow mechanically 

noninvasive measurement of 

human body motion and robot 

kinematics. 

• Stretch, pressure, and curvature 

sensing are combined to 

introduce a family of wearable soft 

(E ~ 1 MPa) and stretchable 

(>350%) sensors that detect 

bending and joint position. 

• Curvature sensors are composed 

of a thin, transparent elastomer 

film embedded with a 

microchannel of conductive liquid. 

• The sensors are demonstrated via 

testing on a human finger joint. 

14:45–14:50 TuCT9.4 

DLR VR-SCAN: A Versatile and Robust 

Miniaturized Laser Scanner for short range 3D- 

Modelling and Exploration in Robotics 

Simon Kielhöfer, Thomas Bahls, Franz Hacker, Tilo Wüsthoff and 

Michael Suppa 

Institute for Robotics and Mechatronics, 


• Miniaturized triangulation based laser scanner with a MEMS scan head 

• “Eye in hand” sensor attachable to the TCP 

• High quality 3D-modelling of objects e.g. for grasp-planning 

• Robust exploration of safe-for-motion (SFM) areas 

of the robot through confidence rating 


–90–


Novel Sensors 

Chair Dario Floreano, Ec. Pol. Federal, Lausanne 

Co-Chair Sanford Meek, Univ. of Utah 

14:50–14:55 TuCT9.5 

Piezoelectric Self-Sensing Technique for 

Tweezer Style End-Effector 

Timothy McPherson and Jun Ueda 

George W. Woodruff School of Mechanical Engineering 


• Piezoelectric tweezers 

use rhombus strain 

amplification to achieve 

macro scale displacement 

• Self-sensing technique 

uses shunt resistance to 

measure discharged 

current 

• Technique is well suited to 

static force and 

displacement 

measurements 

• Experimental verification 

shows 0.15 mm max error 

vs. laser displacement 

sensor 

Piezoelectric Tweezers 

15:00–15:15 TuCT9.7 

Development of 

Omni-Directional and Fast-Responsive 

Net-Structure Proximity Sensor 

Kazuki Terada, Yosuke Suzuki, Hiroaki Hasegawa, 

Satoshi Sone, Aiguo Ming and Makoto Shimojo 

Graduate School of Informatics and Engineering, UEC, JAPAN 

Masatoshi Ishikawa 

Graduate School of Information Science and Technology, Tokyo Univ., JAPAN 

• All photo-transistors on the full surface 

are connected by an unique analog 

computing network circuit. 

• The output signal means the angle and 

the distance of a nearby object. 

• Response time is less than 1 ms, and 

the accuracy of object angle detection 

is less than 3 deg. 

• The sensor is especially available for 

mobile robots to avoid collision with 

rapidly approaching objects. 

“Ring-Shaped Proximity Sensor” 

14:55–15:00 TuCT9.6 

Development of a Low-Profile Sensor Using Electro- 

Conductive Yarns in Recognition of Slippage 

Van Anh Ho*, Daisuke Kondo*, Shima Okada*, Takahiho Araki**, 

Emi Fujita**, Masaaki Makikawa*, and Shinichi Hirai 

(*)Department of Robotics, Ritsumeikan University , Japan 

(**) Research and Development Division, Okamoto Corporation, Japan 

Upper row shows a complete template of a fabric slip sensor and construction of piles. 

Lower row plots the output of the sensor when suffering a rubbing action of human 

fingertip, and application of Discrete Wavelet Transform in slip detection. 

15:15–15:30 TuCT9.8 

Design of a Compact Camera-Orienting 

Mechanism with Flexural Pan and Tilt Axes 

Chao-Chieh Lan and Yi-Chiao Lee 

Dept. of Mechanical Eng., National Cheng Kung University, Taiwan 

Jinn-Feng Jiang, Yi-Jie Chen, and Hung-Yuan Wei 

Metal Industries Research and Development Centre, Taiwan 

• Design and prototype of a camera-orienting mechanism is proposed. 

Pan-tilt motion is achieved by using parallel placed flexural beams to 

actuate a spherical 5-bar mechanism. 

• Flexible mechanism design can reduce the 

number of parts, avoid joint clearance, achieve 

linear output-input relation, and fit irregular 

design domain. 

• Mechanism specifications 

� Size: 42�43�116 mm 

� Weight: 140 g 

� Pan range: �35� 

� Tilt range: �33� 

� Resolution: 0.2� 

� Max. speed: 150�/s 


–91–


–92–

8:00-9:30 



–93– 

Wednesday 


Session WeAT1 ⎯⎯ HRI: Modeling Human Behavior 

Session WeAT2 ⎯⎯ Models and Representation 

Session WeAT3 ⎯⎯ Medical Robotics: Tracking & Detection 

Session WeAT4 ⎯⎯ Symposium: Haptics Interfaces for the Fingertip, Hand, and Arm 

Session WeAT5 ⎯⎯ Symposium: Robot Motion Planning: Achievements and 

Emerging Approaches 

Session WeAT6 ⎯⎯ Symposium: Aerial Robotics: Estimation, Perception and Control 

Session WeAT7 ⎯⎯ Robot Walking 

Session WeAT8 ⎯⎯ Networked Robots 

Session WeAT9 ⎯⎯ Vision: From Features to Applications 

10:00-11:30 

Session WeBT1 ⎯⎯ Socially Assistive Robots 

Session WeBT2 ⎯⎯ Estimation & Sensor Fusion 

Session WeBT3 ⎯⎯ Surgical Robotics 

Session WeBT4 ⎯⎯ Symposium: Hardware and Software Design for Haptic Systems 

Session WeBT5 ⎯⎯ Symposium: Foundations and Future Prospects of Samplingbased 


Session WeBT6 ⎯⎯ Symposium: Aerial Robotics: Control and Planning 

Session WeBT7 ⎯⎯ Passive Walking & Leg-Wheeled Robots 

Session WeBT8 ⎯⎯ Multirobot Systems: Rendezvous & Task Switching 

Session WeBT9 ⎯⎯ Visual Servoing 


14:00-15:30 


–94– 

Wednesday 


(continued) 

Session WeCT1 ⎯⎯ Human-Robot Interaction and Cooperation 

Session WeCT2 ⎯⎯ Pose Estimation & Visual Tracking 

Session WeCT3 ⎯⎯ Robot Safety 

Session WeCT4 ⎯⎯ Symposium: Haptic Feedback and System Evaluation 

Session WeCT5 ⎯⎯ Symposium: Symbolic Approaches to Motion Planning and 

Control 

Session WeCT6 ⎯⎯ Symposium: Marine Robotics: Platforms and Applications 

Session WeCT7 ⎯⎯ Humanoid Control 

Session WeCT8 ⎯⎯ Multirobot Planning 

Session WeCT9 ⎯⎯ Visual & Multi-Sensor Calibration 

16:00-17:30 

Session WeDT1 ⎯⎯ Human-Robot Collaboration 

Session WeDT2 ⎯⎯ Recognition & Prediction of Motion 

Session WeDT3 ⎯⎯ Haptic Rendering & Object Recognition 

Session WeDT4 ⎯⎯ Industrial Forum: Medical Robotics 

Session WeDT5 ⎯⎯ Symposium: Robot Motion Planning: New Frameworks and High 

Performance 

Session WeDT6 ⎯⎯ Symposium: Marine Robotics: Control and Planning 

Session WeDT7 ⎯⎯ Tracking & Gait Analysis 

Session WeDT8 ⎯⎯ Multirobot Coordination & Modular Robots 

Session WeDT9 ⎯⎯ Calibration & Identification

Session WeAT1 Continental Parlor 1 Wednesday, September 28, 2011, 08:00–09:30 

HRI: Modeling Human Behavior 

Chair Kai Oliver Arras, Univ. of Freiburg 

Co-Chair Mario F. Montenegro Campos, Univ. Federal de Minas Gerais 

08:00–08:15 WeAT1.1 

Please do not disturb! Minimum Interference 

Coverage for Social Robots 

Gian Diego Tipaldi and Kai O. Arras 

Social Robotics Lab, University of Freiburg, Germany 

• We address the novel problem 

of human-aware coverage 

• We learn spatio-temporal 

distributions of human activities 

with spatial Poisson processes 

• We use a time dependent TSP to plan 

paths that minimize the interference 

probability with humans 

• Experiments show that the proposed 

coverage planner significantly 

reduces interference in terms of 

durations and numbers of disturbances 

• Applications e.g. for robotic vacuum 

cleaners to learn how to avoid busy 

places at certain times of the day 

08:30–08:45 WeAT1.3 

Generalising Human Demonstration Data by Identifying 

Affordance Symmetries in Object Interaction Trajectories 

Jonathan Claassens 

Council of Industrial and Scientific Research (CSIR), South Africa 

Yiannis Demiris 

Intelligent Systems and Networks, Imperial College London, South Africa 

• When humans interact with objects, their 

hand trajectories often exhibit symmetries. 

• These symmetries may exist along edges 

of tables, around a cup or along the 

handle of an oven. 

• They typically arise from the affordance of 

the affected object. 

• A means to formally specify such 

symmetries and an algorithm to identify 

potential symmetries is proposed. 

• The performance of the algorithm is 

illustrated on data captured from a mocap 

system. 

A Cylindrical Affordance 

Symmetry 

08:15–08:30 WeAT1.2 

Shall We Dance? A Music-driven Approach for 

Mobile Robots Choreography 

Samuel de Sousa Jr and Mario Montenegro Campos 

Departamento de Ciência da Computação, UFMG, Brazil 

• We describe an approach that endows 

a robot to autonomously dance based 

on music. 

•A variability function for estimating the 

turbulence of a block of notes is 

defined which generates specific 

choreographic patterns. 

• Three dancing behaviors: phlegmatic, 

melancholic, and choleric are defined 

and implemented according to the 

turbulence of a block of notes. 


–95– 

Methodology 

08:45–08:50 WeAT1.4 

Human Preferences for 

Robot-Human Hand-over Configurations 

Maya Cakmak 

School of Interactive Computing, Georgia Institute of Technology, USA 

Siddhartha S.Srinivasa, Min Kyung Lee, Jodi Forlizzi, Sara Kiesler 

Robotics Institute & Human-Computer Interaction Institute, 


• Hand-over configuration: Robot and object 

configuration at which the object is 

transferred from the robot to the human. 

• We present a user study that compares 

configurations planned using a kinematic 

model of the human and configurations 

learned from human examples. 

• We find that learned configurations are 

preferred (found to be more natural and 

appropriate), while planned configurations are 

more practical and reachable. 

• We identify three latent variables of preferred 

configurations: arm extension, consistency 

and naturalness. These can be used to plan 

better hand-over configurations. 

Planned (top) and learned (bottom) 

hand-over configurations


HRI: Modeling Human Behavior 

Chair Kai Oliver Arras, Univ. of Freiburg 

Co-Chair Mario F. Montenegro Campos, Univ. Federal de Minas Gerais 

08:50–08:55 WeAT1.5 

Did you see it hesitate? – Empirically Grounded 

Design of Hesitation Trajectories for 

Collaborative Robots 

AJung Moon, Chris A. C. Parker, Elizabeth A. Croft 

H. F. Machiel Van der Loos 

Mechanical Engineering, University of British Columbia, Canada 

• Humans hesitate when unexpected 

conflict of resource occurs. 

• Based on survey validated human 

hesitation gestures, we developed 

characteristic hesitation gesture 

trajectories for a robot. 

• Our study empirically demonstrates that 

the communicative content of human 

hesitation gestures can be transferred 

onto a robot arm. 

• We present the characteristic acceleration 

profile of robotically generated hesitation 

gestures. 



09:00–09:15 WeAT1.7 

Towards an Understanding of Dancers' 

Coupled Body Dynamics for Waltz 

Hongbo Wang and Kazuhiro Kosuge 

Department of Bioengineering and Robotics, Tohoku University, Japan 

• To improve a dance partner robot, two 

waltz dancers’ coupled body dynamics are 

studied 

• Dancers are modeled as linear inverted 

pendulums, connected by a spring and a 

damper 

• Stability and optimal interaction are 

analyzed, under assumptions on 

synchronized, periodic motions 

• Simulations and human-robot experiments 

are conducted to validate our approach 

08:55–09:00 WeAT1.6 

VocaWatcher: Natural Singing Motion Generator 

for a Humanoid Robot 

Shuuji Kajita*, Tomoyasu Nakano**, Masataka Goto**, 

Yosuke Matsusaka*, Shin'ichiro Nakaoka*, and Kazuhito Yokoi* 

*Intelligent Systems Research Institute, AIST, Japan 

**Information Technology Research Institute, AIST, Japan 

• We propose a novel robot motion generator 

that enables a humanoid robot to sing with 

realistic facial expressions and naturally 

synthesized singing voices 

• Our generator uses the HRP-4C and 

consists of two subsystems: VocaWatcher 

for generating singing motions and 

VocaListener for synthesizing singing 

voices 

• VocaWatcher imitates a human singer by 

analyzing a video clip of a human singing, 

recorded by a single video camera 

• VocaListener synthesizes singing voices by 

imitating the pitch and dynamics of the The original human singer and 

human singing 

the robot singer, HRP-4C 

Video clips: http://staff.aist.go.jp/t.nakano/VocaWatcher/ 

09:15–09:30 WeAT1.8 

Understanding human interaction 

for probabilistic autonomous navigation 

using Risk-RRT approach 

Jorge Rios-Martinez 1 , Anne Spalanzani 2 , Christian Laugier 1 

1 INRIA Rhone-Alpes, Grenoble, France 

2 UPMF-Grenoble 2 - INRIA - LIG, France 

• Robot navigation systems must be 

“aware” of the social conventions 

followed by people like respecting 

personal space and o-space 

• This paper proposes a risk-based 

navigation method including both the 

traditional notion of risk of collision and 

the notion of risk of disturbance 

• Results exhibit new emerging behavior 

showing how a robot takes into account 

social conventions in its navigation 

strategy 


–96–


Models and Representation 

Chair Lynne Parker, Univ. of Tennessee 

Co-Chair Christian Schlegel, Univ. of Applied Sciences Ulm 

08:00–08:15 WeAT2.1 

Denoising of Range Images 

using a Trilateral filter and Belief Propagation 

Shuji Oishi, Ryo Kurazume, 

Yumi Iwashita, and Tsutomu Hasegawa 


• Two denoising techniques are 

proposed for a 3D laser scanner 

• We focus on laser reflectance 

obtained as a by-product of range 

image 

• New trilateral filter is proposed for 

suppressing noises in range images 

while preserving geometric features 

• New inpainting technique using belief 

propagation is also proposed to 

recover missing regions in range 

images 

08:30–08:45 WeAT2.3 

3D Crowd Surveillance and Analysis using 

Laser Range Scanners 

Xiaowei Shao, Ryosuke Shibasaki and Yun Shi 

Center for Spatial Information Science, University of Tokyo, Japan 

Huijing Zhao 

State Key Lab of Machine Perception, Peking University, China 

Kiyoshi Sakamoto 

Research and Development Center, East Japan Railway Company, Japan 

• 3D crowd surveillance by using 

swinging laser range scanner 

• Multiple clients are integrated by semiauto 

calibration 

• Automatic and efficient people 

extraction based on improved meanshift 

technique 

• Quantified crowdness analysis in ROIs 

is performed. 

Top to bottom: swinging laser scanner; 

integrated 3D data; people extraction 

08:15–08:30 WeAT2.2 

Representing Actions with Kernels 

Guoliang Luo, Niklas Bergström, 

Carl Henrik Ek and Danica Kragic 

Computer Vision and Active Perception Lab 

Royal Institute of Technology (KTH), Sweden 

Approach 

• Actions as interaction 

• Structural representation 

• Define kernel induced feature 

representation 


–97– 

Benefits 

• Robust to noise 

• Allows for principled inference 

08:45–08:50 WeAT2.4 

4-Dimensional Local Spatio-Temporal Features 

for Human Activity Recognition 

Hao Zhang and Lynne E. Parker 


University of Tennessee, Knoxville, USA 

• A 4-dimensional local spatio-temporal 

feature is proposed that combines both 

intensity and depth information 

• The proposed feature is used for human 

activity recognition with the latent Dirichlet 

allocation (LDA) model as a classifier 

• A new human activity database is created 

using a Kinect installed on a Pioneer robot 

• An average accuracy of 91.50% is 

achieved using the LDA model with the 

proposed feature 

Confusion matrix of human 

activity recognition using Kinect


Models and Representation 

Chair Lynne Parker, Univ. of Tennessee 

Co-Chair Christian Schlegel, Univ. of Applied Sciences Ulm 

08:50–08:55 WeAT2.5 

Conic Fitting based on Stochastic Linearization 

Marcus Baum and Uwe D. Hanebeck 

Intelligent Sensor-Actuator-Systems Lab (ISAS) 

Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany 

• Fitting conics, e.g., ellipses, to noisy measurements 

• Approximation of the implicit measurement eqation with 

an explicit measurement equation 

• Gaussian filter 

09:00–09:15 WeAT2.7 

Managing Execution Variants in Task Coordination 

by Exploiting Design-Time Models at Run-Time 

Andreas Steck and Christian Schlegel 


University of Applied Sciences Ulm, Germany 

• Models created at design- 

time are used by the 

robot at run-time to 

support the decision 

making process. 

• At design-time purpose- 

fully left open variation 

points are bound at run- 

time. 

• Real-world experiments 

http://youtu.be/xtLK-655v7k 

SmartTCL coordinates the whole 

system 

08:55–09:00 WeAT2.6 

Bootstrapping sensorimotor cascades: 

a group-theoretic perspective 

Andrea Censi and Richard M. Murray 

Control & Dynamical Systems 

California Institute of Technology, USA 

• In the bootstrapping problem we study 

agents that can learn to use unknown 

actuators and unknown sensors, starting 

from zero prior information. 

• For example, the figure shows the raw 

intensity values returned by a subset of 

the sensels in a range-finder and a 

camera (can you tell which is which?) 

• In this paper, we formulate the problem as 

a problem of group nuisance rejection. 

Invariance to group nuisances is our 

guiding principle for design. 

• Models are demonstrated on real-world 

data of cameras and range-finders. 


–98– 

Sensels � 

Time � 

09:15–09:30 WeAT2.8 

The Mathematical Model and Control of Human- 

Machine Perceptual Feedback System 

Han Yoon and Seth Hutchinson 


University of Illinois, USA 

• Create interfaces that enable perceptual 

feedback control between a human user 

and a mobile robot. 

• Enhance the perception-to-behavior 

performance of an unskilled human user 

by warping the perceptual display 

• Improve the wellbeing of people suffering 

from the lack of manipulability 

• Protect people like drivers or pilots who 

are impaired by fatigue, boredom, or 

possible substance abuse


Medical Robotics: Tracking & Detection 

Chair Stefano Stramigioli, Univ. of Twente 

Co-Chair Erion Plaku, Catholic Univ. of America 

08:00–08:15 WeAT3.1 

Three-Dimensional Pose Reconstruction of 

Flexible Instruments from Endoscopic Images 

Rob Reilink, Stefano Stramigioli, and Sarthak Misra 


• Position and orientation of flexible 

endoscopic instruments are measured 

without adding sensors 

• Feature points of endoscopic instrument 

are detected within the endoscopic 

image 

• The state of a kinematic model is 

adapted in order to match the feature 

points of the model to observed points in 

the image 

• RMS error of 1.7, 1.2, and 3.6mm in 

resp. horizontal (x), vertical (y) and 

longitudinal (z) directions in colon 

anatomical model. 

08:30–08:45 WeAT3.3 

Comparison of Several Image Features 

for WCE Video Abstract 

Baopu Li1,2 , Max Q.-H. Meng1,2 1. Department of Electronic Engineering, The Chinese University of Hong Kong 

2. Shenzhen Institute of Advanced Technology, 

The Chinese Academy of Science, China 

• Wireless Capsule endoscopy (WCE) is a revolutionary technique to 

examine the diseases in small intestine 

• Deduction of the review time for a WCE video is needed 

• Several image features are compared to summarize a WCE 

• Preliminary experiments show that textural and motion features maybe 

suitable for a WCE video abstract 

• Clinical validation is a part of future work 

08:15–08:30 WeAT3.2 

Detection of Curved Robots using 3D 

Ultrasound 

Hongliang Ren, Nikolay V. Vasilyev and Pierre E. Dupont 

Department of Cardiac Surgery, Children's Hospital Boston 

Harvard Medical School, USA 

• Goal is image-based continuum robot 

tracking and servoing using real-time 3D 

ultrasound 

• Existing detection algorithms apply to 

robotic instruments with straight shafts, 

but do not extend to continuum robots that 

curve along their length 

• Problem complexity is reduced by 

decomposing arc detection into sequence 

of plane and circle detection problems. 

• Proposed approach is parallelizable. 


–99– 

3D Ultrasound volume and 

detected curved robot 

08:45–08:50 WeAT3.4 

Toward Development of 3D Surgical Mouse 

Paradigm 

Xiaochuan Sun and Shahram Payandeh 

Experimental Robotics and Imaging Laboratory 

School of Engineering Science 

Simon Fraser University, Canada 

• Monocular systems imaging systems are 

commonly utilized in In minimally invasive 

surgery. 

• The lack of 3D visual perception offers a 

natural challenge for innovation design 

opportunities developing in surgeoncomputer 

interface. 

• Design of a robust monocular-based 

image tracking of the surgical instruments 

is an essential first step. 

• We introduce a novel 3D mouse paradigm 

and compare four different image tracking 

schemes. 

3D mouse conceptual diagram


Medical Robotics: Tracking & Detection 

Chair Stefano Stramigioli, Univ. of Twente 

Co-Chair Erion Plaku, Catholic Univ. of America 

08:50–08:55 WeAT3.5 

3D Thread Tracking for Robotic Assistance in 

Tele-surgery 

Nicolas Padoy, Gregory D. Hager 

The Johns Hopkins University, Baltimore, MD, USA 

{padoy,hager}@jhu.edu 

• Automatic third arm assistance 

• Thread modeling and tracking with nonuniform 

rationale B-splines 

• Energy approximation and minimization 

using discrete MRF optimization 

• Automatic scissors command for the da 

Vinci robot 

09:00–09:15 WeAT3.7 

In-vitro Three Dimensional Vasculature Modeling 

Based on Sensor Fusion between Intravascular 

Ultrasound and Magnetic Tracker 

Chaoyang Shi, Carlos Tercero, Seiichi Ikeda, Toshio Fukuda 

Micro-Nano Systems Engineering, Nagoya University, Japan 

Kimihiro Komori and Kiyohito Yamamoto 

Graduate School of Medicine, Nagoya University, Japan 

• Proposed the new sensor fusion 

technology between intravascular 

ultrasound (IVUS) and magnetic 

trackers for intravascular surgery 

• Integrated the hybrid probe and 

measured the disturbances between 

two sensors 

• Performed in-vitro experiments by 

adopting a silicone model of 

descending aorta, and reconstructed 

the corresponding 3D model 

Fig. a) Hybrid probe consisting of magnetic 

tracker and IVUS probe; b) Silicone model 

for in-vitro experiments; c) Reconstructed 

model. 

08:55–09:00 WeAT3.6 

Ultrasound image features of the wrist 

are linearly related to finger positions 

Claudio Castellini and Georg Passig 

Institute of Robotics and Mechatronics 

DLR - German Aerospace Research Center 

Oberpfaffenhofen, Germany 

• ultrasound images of the human wrist 

and finger positions are recorded using 

a commercial ultrasound machine and 

a dataglove 

• first-order local linear approximations of 

the grey levels are extracted from the 

images, and 

• a strong linear relationship is detected 

between them and finger positions 

• finger positons are then predicted in 

real time 

• this includes fingers flexion/extension 

and thumb adduction/rotation 

• the system is robust to downsampling 


–100– 

typical values of true and 

predicted pinkie position 

• main forseen application is 

on transradial amputees: 

1.treatment of phantom limb pain 

2.visualisation of the imaginary limb 

3.control of a hand prosthesis 

09:15–09:30 WeAT3.8 

Surgical Tools Pose Estimation for a Multimodal 

HMI of a Surgical Robotic Assistant 

Belén Estebanez, Enrique Bauzano and Víctor F. Muñoz 

System Engineering and Automation, University of Malaga, Spain 

• The recognition of a surgeon’s maneuver 

through Hidden Markov Models (HMM) 

requires the tracking of the surgical tools. 

• Minimization of the measured occluded 

areas is needed to control autonomous 

movements of a robotic assistant through 

gestures. 

• Location of each surgeon’s tool may be 

estimated with Multiple Extended Kalman 

Filters (MEKF), one for each gesture the 

maneuver is composed of. 

• Estimated location depends on the most 

probable maneuver the surgeon may be 

carrying out.

Session WeAT4 Continental Ballroom 4 Wednesday, September 28, 2011, 08:00–09:30 

Symposium: Haptics Interfaces for the Fingertip, Hand, and Arm 

Chair Katherine J. Kuchenbecker, Univ. of Pennsylvania 

Co-Chair Marcia O'Malley, Rice Univ. 

08:00–08:15 WeAT4.1* 

Semi-Plenary Invited Talk: Surface Haptics: Virtual 

Touch on Physical Surfaces 

Edward Colgate, Northwestern University 

08:30–08:45 WeAT4.3 

Weight and Friction Display Device 

by Controlling the Slip Condition of a Fingertip 

Yuichi Kurita 

Graduate School of Engineering, Hiroshima University, Japan 

Satoshi Yonezawa, Atsutoshi Ikeda and Tsukasa Ogasawara 

Graduate School of Information Science, 

Nara Institute of Technology, Japan 

• weight and friction illusions are generated 

by controlling ``eccentricity’’ of the contact 

surface between a fingertip and a rigid 

plate. 

• The eccentricity is a quantitative index of 

the slip condition calculated from image 

processing of captured images. 

• The desired eccentricity profile for the 

target weight and friction was obtained by 

modeling human’s grip/load force profiles. 

• Human experiments showed that the 

proposed device successfully presents the 

weight/friction illusions. 

08:15–08:30 WeAT4.2 

Semi-Plenary Invited Talk: Surface Haptics: Virtual 

Touch on Physical Surfaces 

Edward Colgate, Northwestern University 

08:45–08:50 WeAT4.4 

On-line Bio-impedance Identification of Fingertip 

Skin for Enhancement of Electrotactile Based Haptic 

Rendering 

John Gregory*, Yantao Shen**, and Ning Xi † 

*Wintek Electro-Optics Co., Ann Arbor, Michigan, USA 

** Dept. of Electrical and Biomedical Engr., University of Nevada, Reno, USA 

† Dept. of Electrical and Computer Engr., Michigan State University, USA & 

Dept. of Manufacturing Engineering and Engineering Management, The City 

University of Hong Kong, China 

• A new constant-voltage-driver 

(CVD) based electrotactile haptic 

rendering system is developed 

• The system owns the feature of 

on-line identifying bio-impedance 

of fingertip skin for haptic/tactile 

preference tuning 

• The identification method is 

based on a discrete-time 

extended least squares iterative 

approach with forgetting factor 

• Experimental results 

demonstrate the performance of 

both the system and the 

identification method 


–101– 

Adaptive fingertip skin bio-impedance 

identification in z-domain


Symposium: Haptics Interfaces for the Fingertip, Hand, and Arm 

Chair Katherine J. Kuchenbecker, Univ. of Pennsylvania 

Co-Chair Marcia O'Malley, Rice Univ. 

08:50–08:55 WeAT4.5 

Design of an MRI Compatible Haptic Interface 

• A plastic, MRI Compatible, 1-axis, fiber 

optic force sensor for robotic 

applications in MRI environment 

• Its compact design employs a 

displacement amplifying compliant 

mechanism for more accurate sensing 

• Samples made of delrin and ABSplastic 

are tested, both samples 

confirmed MRI safety requirements 

• Delrin sample is superior to ABSplastic 

in accuracy and uniaxial 

sensitivity 

Melih Turkseven and Jun Ueda 

George W. Woodruff School of Mechanical Engineering, 


The proposed MRI compatible 

force sensor 

09:00–09:15 WeAT4.7 

Wide-bandwidth Bilateral Control 

Using Two Stage Actuator Systems: 

Evaluation Results of a Prototype 

Saori Kokuryu, Masaki Izutsu, Norihiro Kamamichi and 

Jun Ishikawa 

Department of Robotics and Mechatoronics, Tokyo Denki University, Japan 

• Developed a prototype of the proposed 

two stage actuator system for widebandwidth 

bilateral control 

• Proposed a coordinated motion control via 

control bandwidth separation method 

• Successfully Implemented a mechanical 

impedance control, the mass of which was 

0.01 kg, the damping 1.6 N/(m/s) without 

stiffness 

• Established a basis for wide-bandwidth 

bilateral control using lightly-moving 

actuator system 

Second Seco S 

stage 

First stage 

Ball screw 

AC 

motor 

Encoder 

Laser displacement sensor 

Prototype using two inexpensive 

actuators, i.e., voice coil motor 

and AC motor with ball screw 

08:55–09:00 WeAT4.6 

Force Producibility Improvement of Redundant 

Parallel Mechanism for Haptic Applications 

Jumpei Arata, Norio Ikedo and Hideo Fujimoto 

Nagoya Institute of Technology, Japan 

• Parallel mechanism is widely applied to 

haptic applications for advantageous 

benefits such as high rigidity, output force 

and backdrivability . 

• In our past study, we proposed a new 

redundant parallel mechanism “DELTA-R” 

for a haptic device. 

• In this paper, we propose an improvement 

method of force producibility by using the 

redundant DOF. 

• The forward kinematic model was 

modified by taking into account the 

geometric conditions of the mechanism. 

• Extremal Force Diagram (EFD) and 

experimental results using prototype 

clearly showed the effectiveness of the 

proposed method. 


–102– 

Redundant PM DELTA-R 

and its improved force producibility 

09:15–09:30 WeAT4.8 

A New Generation of Ergonomic Exoskeletons – 

The High-Performance X-Arm-2 

André Schiele 

Telerobotics & Haptics Laboratory, ESA, Netherlands 

Gerd Hirzinger 

Institute of Robotics & Mechatronics, DLR, Germany 

• New, fully actuated and ergonomic 

haptic exoskeleton for space robotics 

Telepresence 

• Human-centered design 

• Low mass and high-power density 

implementation of actuators through 

combination of directly integrated and 

Bowden Cable relocated drives 

• Detailed performance characterization of 

mechatronic implementation through 

Bode plots and contact experiments 

X-Arm-2 Exoskeleton front (top) and 

back-side view (bottom)


Symposium: Robot Motion Planning: Achievements and Emerging Approaches 

Chair Ron Alterovitz, Univ. of North Carolina at Chapel Hill 

Co-Chair Maxim Likhachev, Carnegie Mellon Univ. 

08:00–08:15 WeAT5.1* 

Semi-Plenary Invited Talk: 50 Years of Robotics: 


Tomas Lozano-Perez, MIT 

08:30–08:45 WeAT5.3 

Conflict-Free Route Planning 

in Dynamic Environments 

Adriaan W. ter Mors 

Delft University of Technology, The Netherlands 

• Optimal route planning on a roadmap for a single robot, in 

polynomial time 

• Prioritized approach: a robot plans around the routes of higherpriority 

robots 

• Unexpected delays for one robot can propagate to others 

• Pareto- and non-Pareto optimal plan repair mechanisms are 

compared 

Two robots traverse a shared roadmap 

of limited-capacity resources 

08:15–08:30 WeAT5.2 

Semi-Plenary Invited Talk: 50 Years of Robotics: 


Tomas Lozano-Perez, MIT 

08:45–08:50 WeAT5.4 

Kinodynamic Motion Planning with 

State Lattice Motion Primitives 

Mihail Pivtoraiko and Alonzo Kelly 


• We present an approach to automatic 

generation of mo-tion primitives for 

kinodyna-mic motion planning; 

• State lattice motion primitives feature a 

number of advanta-ges, including enabling 

reuse of computation (e.g. D*) in differentially 

constrained motion planning; 

• We demonstrate incremental planning for 

a dynamics sys-tem: a mobile robot 

moving on slippery surface with significant 

drift. 


–103–


Symposium: Robot Motion Planning: Achievements and Emerging Approaches 

Chair Ron Alterovitz, Univ. of North Carolina at Chapel Hill 

Co-Chair Maxim Likhachev, Carnegie Mellon Univ. 

08:50–08:55 WeAT5.5 

Efficient Motion Planning for Manipulation Robots 

in Environments with Deformable Objects 

Barbara Frank, Cyrill Stachniss, 

Nichola Abdo, and Wolfram Burgard 

Department of Computer Science, University of Freiburg, Germany 

• Approach to online manipulator motion 

planning considering deformable objects 

• Efficient estimation of deformation cost 

along a trajectory using Gaussian process 

(GP) regression 

• GP training using a deformation simulation 

based on finite element methods 

• Experiments illustrate an accurate cost 

estimation and online planning capabilities 

Our robot deforming the leaves of 

a plant along a trajectory 

09:00–09:15 WeAT5.7 

A Simplified Model of RRT Coverage for 

Kinematic Systems 

Joel M Esposito 

Systems Engineering, United States Naval Academy 

• Rapidly Exploring Random Trees explore 

the state space..but how quickly? 

• We model state space coverage as a 

random discrete time process 

• Under two simplifying assumptions we 

obtain a closed form solution for expected 

coverage as a function of number of 

nodes 

• Experimental results suggest model is 

valid for holonomic systems in expansive 

configuration spaces. 

• Model can be used as termination criteria, 

or as a measure of problem difficulty 

08:55–09:00 WeAT5.6 

Learning Dimensional Descent planning for a 

highly-articulated robot arm 

Paul Vernaza and Daniel D. Lee 

GRASP Laboratory, University of Pennsylvania, USA 

• Learning Dimensional Descent (LDD) 

solves high-dimensional planning 

problems by solving a sequence of 

low-dimensional DP problems 

• Learning-based technique 

automatically identifies and exploits 

low-dimensional structure 

• Experiments with PR2 robot 

demonstrate much higher-quality 

solutions obtained than with samplingbased 

planners and smoothing 

• Open-source implementation available 


–104– 

Illustration of one step of LDD algorithm. 

Solution generated in next step is 

minimum-cost path restricted to manifold 

of previous path swept along learned 

basis directions. 

09:15–09:30 WeAT5.8 

Space-Filling Trees: A New Perspective on 

Incremental Search for Motion Planning 

James J. Kuffner 


& Google Research, Google Inc., USA 


Dept. of Computer Science, Univ. of Illinois at Urbana-Champaign, USA 

• The concept of “Space-Filling Trees” is 

introduced and analyzed in the context of 

sampling-based motion planning. 

• Space-filling trees are analogous to 

space-filling curves, but with a branching, 

tree-like structure that connects a single 

point in a continuous space to every other 

point by a path of finite length. 

• Rapidly-exploring Random Trees (RRTs) 

can be considered stochastic variants of 

space-filling trees, and compared in terms 

of overall efficiency of exploration. 

• Key open problems remain, such as 

applying space-filling trees to the design 

and analysis of sampling-based planners. 

Three Iterations of the triangle 

and square space-filling trees.


Symposium: Aerial Robotics: Estimation, Perception and Control 

Chair Nathan Michael, Univ. of Pennsylvania 

Co-Chair Pieter Abbeel, UC Berkeley 

08:00–08:15 WeAT6.1* 

Semi-Plenary Invited Talk: Do It Yourself Drones 

Chris Anderson, Wired Magazine 

08:30–08:45 WeAT6.3 

Multiple-Objective Motion Planning 

for Unmanned Aerial Vehicles (UAVs) 

Sebastian Scherer and Sanjiv Singh 


• Want to perform various missions (reach a goal, search for landing 

sites, approach LZs), however mission cannot be expressed as goal 

seeking behavior 

• Propose a consistent framework and algorithm to plan with a 

combination of multiple objectives 

• Show results from performing realistic missions in simulation 

08:15–08:30 WeAT6.2 

Semi-Plenary Invited Talk: Do It Yourself Drones 

Chris Anderson, Wired Magazine 

08:45–08:50 WeAT6.4 

Bilateral Teleoperation of Multiple UAVs with 

Decentralized Bearing-only Formation Control 

Antonio Franchi 1 , Carlo Masone 1 

Heinrich H. Bülthoff 1,2 , and Paolo Robuffo Giordano 1 

1 Max Plank Institute for Biological Cybernetics, Germany 

2 Department of Brain and Cognitive Engineering, Korea University, Korea 

• Decentralized 

multi-master/multi-slave 

bilateral teleoperation of 

groups of UAVs: 

1. Human controls collective motion 

2. UAVs autonomously control shape 

• Only relative bearing measures 

used (no global localization, no 

distances) 

• Rigorous analysis of minimally-rigid sets 

of 3D bearing-formations and associated 

decentralized formation control 

• Stable design of force feedback for the human operator in 

order to increase telepresence 


–105– 

Human-hardware-in-the-loop 

simulations and experiments 

with quadrotors 

and 2 haptic devices


Symposium: Aerial Robotics: Estimation, Perception and Control 

Chair Nathan Michael, Univ. of Pennsylvania 


08:50–08:55 WeAT6.5 

Modeling and Decoupling Control 

of the CoaX Micro Helicopter 

Péter Fankhauser, Samir Bouabdallah, 

Stefan Leutenegger, Roland Siegwart 


• A nonlinear model for a coaxial micro 

helicopter based on gray-box modeling 

and parameter identification is presented 

• The model accounts for hover and 

cruise flight situations and captures the 

off-axis dynamics and the dynamics of 

the stabilizer bar 

• A test bench and a vision based tracking 

system are used for parameter 

identification and model validation 

• A decoupling controller is developed and 

implemented which shows to increase 

the accuracy of flight trajectories 

09:00–09:15 WeAT6.7 

Deterministic Initialization of Metric 

State Estimation Filters for Loosely-Coupled 

Monocular Vision-Inertial Systems 

Laurent Kneip, Stephan Weiss, and Roland Siegwart 

Autonomous Systems Lab, ETH Zurich 

• Deterministic scale factor computation of monocular visual SLAM 

• Deterministic computation of orientation with respect to gravity direction 

• Performed via a derivation of delta-velocities from 

• Numerical differentiation of visual pose 

• Integration of inertial data 

• Robustness analysis for noise 

and degenerate motions 

• Successful results on simulated 

and real data 

• Performance tests on filter initialization 

08:55–09:00 WeAT6.6 

On Active Target Tracking and Cooperative 

Localization for Multiple Aerial Vehicles 

Fabio Morbidi, Gian Luca Mariottini 

ASTRA Robotics Laboratory 

Department of Computer Science & Engineering 

University of Texas at Arlington, USA 

• Cooperative active target-tracking 

for a team of aerial vehicles 

equipped with 3-D range-finding 

sensors 

• Gradient-based controllers are 

designed for each vehicle 

• The Kalman-Bucy filter is used for 

estimation fusion 

• Analytical derivation of lower and 

upper bounds on the target’s 

position uncertainty 

• Active Cooperative Localization 

and Multi-target Tracking (ACLMT) 

minimizes both targets’- and 

robots’- position uncertainty 


–106– 

Four aerial vehicles cooperatively 

track a target to estimate its position 

with minimum uncertainty 

09:15–09:30 WeAT6.8 

Collaborative Stereo 

Markus W. Achtelik*, Stephan Weiss*, Margarita Chli*, 

Frank Dellaert † , and Roland Siegwart* 

*ETH Zurich, Autonomous Systems Lab, Switzerland 

† Georgia Institute of Technology, School of Interactive Computing, USA 

• Two MAVs, each equipped with a monocular camera and an IMU, 

form a variable baseline stereo rig 

• Extrinsic calibration estimation with absolute scale baseline by 

incorporating IMU information from both MAVs 

• Vision / IMU data fusion with EKF framework – only dependent on 

IMU and relative vision measurements


Robot Walking 

Chair Luis Sentis, The Univ. of Texas at Austin 

Co-Chair Shinya Aoi, Kyoto Univ. 

08:00–08:15 WeAT7.1 

Self-stabilization Principle of Mechanical Energy 

Inherent in Passive Compass Gait 

Fumihiko Asano 

School of Information Science, 

Japan Advanced Institute of Science and Technology, Japan 

• The self-stabilization principle 

underlying passive compass gait is 

investigated from the mechanical 

energy point of view. 

• It is numerically shown that a passive 

compass gait is monotonically 

stabilized not by means of the state 

variables but by means of the total 

mechanical energy. 

• The linearized mechanical energy 

(LME) is defined and the mechanism 

of monotonic convergence is 

mathematically investigated. 

08:30–08:45 WeAT7.3 

A Walking Stability Controller with Disturbance 

Rejection Based on CMP Criterion and Ground 

Reaction Force Feedback 

Richard Beranek, Henry Fung and Mojtaba Ahmadi 

Department of Mechanical and Aerospace Engineering, Carleton University, 

Canada 

• Robustness to external disturbances in 

bipedal control remains limited 

• Centroidal Moment Pivot (CMP) with 

ground reaction force feedback is used to 

modify the reference COG trajectory to 

increase stability 

• Simulation results results show the 

controller is more robust to a sagital 

disturbance than a ZMP based controller 

With ground reaction force 

feedback, CMP can be used to 

compensate for unbalanced 

moments acting on the system 

08:15–08:30 WeAT7.2 

Model-Based Velocity Control 

for Limit Cycle Walking 

Tuomas Haarnoja 

Smart Machines, VTT Technical Research Centre of Finland, Finland 

José-Luis Peralta-Cabezas and Aarne Halme 

Department of Automation and Systems Technology, Aalto University, Finland 

• Limit Cycle Walking (LCW) is an energy efficient but restrictive method 

of locomotion for legged robots 

• The paper proposes several ideas for controlling the velocity of a LCW 

robot 

• The methods are tested in simulations and the results are compared in 

the sense of their energy efficiency and achievable velocity range 

08:45–08:50 WeAT7.4 

Perturbation Theory to Plan Dynamic Locomotion 

in Very Rough Terrain 

• Motivation 

• Planning 

Luis Sentis and Benito Fernandez 

The University of Texas at Austin, USA 

• Prediction 

• Results 


–107–


Robot Walking 

Chair Luis Sentis, The Univ. of Texas at Austin 

Co-Chair Shinya Aoi, Kyoto Univ. 

08:50–08:55 WeAT7.5 

Generation of adaptive splitbelt treadmill 

walking by a biped robot using nonlinear 

oscillators with phase resetting 

Shinya Aoi 1 , Soichiro Fujiki 1 , Tsuyoshi Yamashita 1 

Takehisa Kohda 1 , Kei Senda 1 , and Kazuo Tsuchiya 2 

1 Kyoto University, Japan, 2 Doshisha University, Japan 

• We designed a biped robot, whose 

joints are controlled by nonlinear 

oscillators with phase resetting 

• Stable walking on splitbelt treadmill 

was generated despite various 

speed conditions of belts 

• Speed discrepancy caused shift of 

phase differences between leg 

movements and modulation of duty 

factors 

• This adaptability was due to 

modulation of locomotion rhythm and 

phase by phase resetting 

Biped robot walking on 

splitbelt treadmill 

09:00–09:15 WeAT7.7 

Multi-objective Parameter CPG Optimization for 

Gait Generation of a Quadruped Robot 

Considering Behavioral Diversity 

Miguel Oliveira, Cristina P. Santos, 

Vítor Matos, and Manuel Ferreira 

Industrial Electronics Department, Minho University , Portugal 

Lino Costa 

Production Systems Department, Minho University, Portugal 

• Multi-objective optimization system 

that combines bio-inspired Central 

Patterns Generators (CPGs) and a 

multi-objective evolutionary 

algorithm(NSGAII) 

• CPGs are modeled as autonomous 

differential equations that generate the 

necessary limb movement 

• four conflicting objectives are 

considered: vibration, velocity, wide 

stability margin and behavioral 

diversity 

08:55–09:00 WeAT7.6 

Experimental verification of hysteresis in gait 

transition of a quadruped robot driven by 

nonlinear oscillators with phase resetting 

Shinya Aoi 1 , Soichiro Fujiki 1 , Daiki Katayama 1 , Tsuyoshi 

Yamashita 1 , Takehisa Kohda 1 , Kei Senda 1 , Kazuo Tsuchiya 2 

1 Kyoto University, Japan, 2 Doshisha University, Japan 

• We designed a quadruped robot, 

whose legs are controlled by 

nonlinear oscillators with phase 

resetting 

• Walk and trot patterns were generated 

through interactions among robot 

dynamics, oscillator dynamics, and 

environment 

• Walk-trot transition is induced by 

changing the walking speed 

• Hysteresis appears in the gait 

transition 


–108– 

Quadruped robot and 

hysteresis in walk-trot transition 

09:15–09:30 WeAT7.8 

A sparse model predictive control formulation 

for walking motion generation 

Dimitar Dimitrov and Alexander Sherikov 

Orebro University, Sweden 

Pierre-Brice Wieber 

INRIA-Grenoble, France 

• Walking motion generation with variable 

sampling time and CoM height 

• MPC formulation that does not depend 

on forming an objective offline 

• Faster QP solution



Chair C. S. George Lee, Purdue Univ. 

Co-Chair Ron Lumia, Univ. of New Mexico 

08:00–08:15 WeAT8.1 

Mobility and Routing Joint Design for Lifetime 

Maximization in Mobile Sensor Networks 

Shengwei Yu and C. S. George Lee 

School of Electrical and Computer Engineering 

Purdue University, USA 

• Simultaneously design mobility and 

routing strategies of robotic sensor nodes 

to improve the lifetime of a mobile sensor 

network. 

• The non-convex problem was solved 

distributedly by a series of convex 

approximations and a novel algorithm. 

• Computer simulations demonstrated quick 

convergence to the solution and the 

lifetime of the network was improved 

tremendously. 

• The proposed joint design method has an 

edge over other methods in lifetime 

maximization of mobile sensor networks. 

08:30–08:45 WeAT8.3 

Optimal Maintenance Strategy 

in 

Fault-Tolerant Multi-Robot Systems 

Satoshi Hoshino and Hiroya Seki 

Chemical Resources Laboratory, Tokyo Institute of Technology, Japan 

Jun Ota 

Research into Artifacts, Center for Engineering (RACE), The University of 

Tokyo, Japan 

• Optimal maintenance strategy for multirobot 

systems on the basis of reliability 

engineering is proposed 

• Robots are allowed to undergo preventive 

maintenance at optimal intervals 

• Fault tolerance of the system is ensured 

as the number of robots is increased 

• Highest performance of the system with 

many robots is successfully maintained 

Robot’s availability and failure 

rate curves based on shape 

parameters 

08:15–08:30 WeAT8.2 

Decentralized Multi-Vehicle Path Coordination 

under Communication Constraints 

Pramod Abichandani and Moshe Kam 

Electrical and Computer Engineering Department, Drexel University, USA 

Hande Benson 

Department of Decision Sciences, Drexel University, USA 

• Decentralized framework to generate time 

optimal speed profiles for a group of path 

constrained vehicle robots 

• Paths modeled as cubic splines 

• Problem modeled as a Receding Horizon 

Mixed Integer Nonlinear Programming 

problem (RH-MINLP) 

• Constraints on kinematics, dynamics, 

collision avoidance and communication 

connectivity 

• Results for up to ten (10) robots 

08:45–08:50 WeAT8.4 

Distributed Control of Multi–Robot Systems with 

Global Connectivity Maintenance 

Lorenzo Sabattini 

Department of Electronics, Computer Sciences and Systems 

University of Bologna, Italy 

Nikhil Chopra 

Department of Mechanical Engineering and Institute for Systems Research 

University of Maryland, College Park, MD, USA 

Cristian Secchi 

Department of Sciences and Methods of Engineering 

University of Modena and Reggio Emilia, Italy 

• Decentralized estimation of the algebraic c 

connectivity 

• Connectivity maintenance is formally 

guaranteed 

• Formation control and rendezvous 

applications 


–109–



Chair C. S. George Lee, Purdue Univ. 

Co-Chair Ron Lumia, Univ. of New Mexico 

08:50–08:55 WeAT8.5 

RSSI-based Physical Layout Classification and 

Target Tethering in Mobile Ad-hoc Networks 

Prashant Reddy 

Machine Learning Department, Carnegie Mellon University, USA 

Manuela Veloso 

Computer Science Department, Carnegie Mellon University, USA 

• LANdroids are simple inexpensive robots 

equipped only with a Wi-Fi radio sensor. 

• Problem: Using LANdroids to maintain 

connectivity between a static Gateway 

and mobile Targets (a.k.a. Target 

Tethering). 

• Challenge: The Wi-Fi RSSI signal is a 

very poor indicator of physical distance; 

how can we better use the RSSI signal? 

• Approach: Use SVM-based classification 

of RSSI signals amongst multiple 

LANdroids to categorize their physical 

layout into known Cluster Geometries. 

Then, use Q-learning to learn tethering 

policies based on those Geometries. 

09:00–09:15 WeAT8.7 

Leader-Follower Formation Control of 

Nonholonomic Robots with Fuzzy Logic Based 

Approach for Obstacle Avoidance 

Jawhar Ghommam1, Hasan Mehrjerdi 2 and Maarouf Saad2 1Research Unit on Mechatronics and Autonomous Systems 

ENIS-Sfax-Tunisia 

2Ecole de technologie supérieure 

1100, rue Notre-Dame Ouest Montreal, Quebec 

• A combination of the virtual vehicle and 

trajectory tracking approach is used. 

• A virtual vehicle is steered in such a way it 

stabilizes to a shifted reference 

position/heading defined by the leader 

• Velocity of the virtual vehicle is then used 

for further use in designing control law for 

the follower independent from the 

measurement of leader's velocity. 

• Ensuring the safety of robots while moving 

in a dynamic environment. 

• Obstacle avoidance scheme is introduced 

using fuzzy logic. 

Leader follower based virtual 

vehicle approach 

08:55–09:00 WeAT8.6 

Heterogeneous Sensor Network 

for Prioritized Sensing 

R. Andres Cortez and John Wood 

Department of Mechanical Engineering, University of New Mexico, USA 

Rafael Fierro 

Department of Electrical & Computer Eng., University of New Mexico, USA 

• Heterogeneous sensor network: 

• sensing agents 

• communication relays 

• Derive communication constraints within 

the network that guarantee network 

connectivity 

• Develop an algorithm that allows for 

adding communication links to the minimal 

spanning tree of the heterogeneous 

proximity graph 

• Combine a prioritized search algorithm 

and the communication constraints to 

provide a decentralized prioritized sensing 

control algorithm 


–110– 

Motion constraint set for a relay 

agent w.r.t. the network 

09:15–09:30 WeAT8.8 

On the Convergence of Braitenberg vehicle 3a 

Immersed in Parabolic Stimuli 

Iñaki Rañó 

Institut für Neuroinformatik, RUB, Germany 

• Braitenberg vehicle 3a models target 

seeking in animals. 

• Two sensors are directly connected to the 

wheels in a decreasing (-) way. 

• We perform the first theoretical analysis of 

the behavior. 

• The trajectory is described by non-linear 

differential equations. 

• For circular symmetry we identify the 

conditions when the trajectory; moves away 

from, approaches or circles around the 

target. 

• For parabolic situations the vehicle 

approaches the target along a principal 

direction. 

Braitenberg vehicle 3a


Vision: From Features to Applications 

Chair Jana Kosecka, George Mason Univ. 

Co-Chair Mohammad Mahoor, Univ. of Denver 

08:00–08:15 WeAT9.1 

Yield Estimation in Vineyards 

by Visual Grape Detection 

Stephen Nuske, Supreeth Achar, 

Srinivas Narasimhan, Sanjiv Singh 


Terry Bates 

Lake Eerie Research and Extension Laboratory, Cornell University, USA 

• Fine grained knowledge of 

yields can allow better 

management of vineyards 

• Current industry practice for 

yield estimation is destructive 

and spatially sparse 

• We use computer vision to 

count grapes and predict yield 

• Our method can be scaled to 

provide non-destructive, highresolution, 

yield estimates 

across large vineyards 

• We can predict yield of 

individual vineyard rows to 

within 9.8% of actual harvest 

08:30–08:45 WeAT9.3 

A Rotation Invariant Feature Descriptor O-DAISY 

and its FPGA Implementation 

Jan Fischer, Alexander Ruppel, Florian Weißhardt 

and Alexander Verl 

Robot Systems Department, Fraunhofer Institute for 

Manufacturing Engineering and Automation (IPA), Germany 

• O-DAISY, a rotational invariant 

extension of the DAISY feature 

descriptor 

• Outperforms other fast-to-compute 

descriptors like SURF and BRIEF 

• Description of FPGA 

implementation to achieve realtime 

performance of the descriptor 

calculation 

• Pipelined FPGA architecture 

enabling the processing of one 

descriptor per clock at 125 MHz 

Matching accuracy as a function of the 

rotation angle for an artificially rotated 

wall image using the rotation variant 

original DAISY (black), and O-DAISY 

variants (red and magenta) 

08:15–08:30 WeAT9.2 

A Novel Learning-based Approach for Local 

Patch Recognition 

Ce Gao, Yixu Song, Peifa Jia 

Department of Computer Science & Technology 

Tsinghua University, China 

• We propose a novel learningbased 

feature matching 

approach for Robot Visual 

Learning. 

• An improved version of FAST-9 

is adopted to extract keypoints 

quickly. 

• It identifies keypoints belong to 

different objects or background 

by color and texture 

representation. 

• 3a two-stage multilayer ferns 

classifier is trained to recognize 

the local patches. 


–111– 

Matching result for BSD and PASCAL. The color 

points represent correct matching, and different 

colors represent different objects. 

08:45–08:50 WeAT9.4 

Adaptive Multi-Affine (AMA) Feature-Matching Algorithm 

and its Application to Minimally-Invasive Surgery Images 

G. Puerto1 , M. Adibi2 , J.A. Cadeddu2 , G.L. Mariottini1 1Dept. of Comp. Science and Eng., Univ. of Texas at Arlington, USA 

2Dept. of Urology, Univ. of Texas Southwestern Med. Center, USA 

• Existing methods match only few points btw. images of non-planar object 

• Our algorithm (AMA) can match increased number of image features 

on the entire 3-D object surface 

• Multi-Affine transformations used 

to recover from any feature-tracking 

loss (e.g., occlusion) 

• Affine transformations are found 

adaptively to maximize no. of 

matched features. 

• Extensive experimental validation 

with Minimally-Invasive Surgery 

videos. 

Above, state-of-art method matches few features (dots) and one 

affine transformation (indicated w/ arrow). Below, our method 

matches more features distributed over the entire object surface.


Vision: From Features to Applications 

Chair Jana Kosecka, George Mason Univ. 

Co-Chair Mohammad Mahoor, Univ. of Denver 

08:50–08:55 WeAT9.5 

Video Stabilization Using SIFT-ME Features and 

Fuzzy Clustering 

Kevin L. Veon, Mohammad H. Mahoor, and Richard M. Voyles 

Department of Electrical and Computer Engineering 

University of Denver, Denver, CO, USA 

• The orientation of SIFT features is used to 

estimate rotation. 

• Fuzzy set theory is used to separate local 

motion and global motion. 

• Kalman filtering is used to estimate 

desired motion. 

• Scenarios with combinations of 

translation, rotation, local motion, and 

desired motion are presented. 

• Qualitative and quantitative measures are 

used to evaluate stabilization 

performance. 

Example Unstable (top) and 

stabilized (bottom) video frames. 

09:00–09:15 WeAT9.7 

A Learning Algorithm for Visual Pose Estimation 

of Continuum Robots 

Austin Reiter, Peter K. Allen, and Konstantinos Iliopoulos 

Dept. of Computer Science, Columbia University, USA 

Roger E. Goldman 

Dept. of Biomedical Engineering, Columbia University, USA 

Andrea Bajo and Nabil Simaan 

Dept. of Mechanical Engineering, Vanderbilt University, USA 

• Estimate the configuration of a continuum 

robot using vision 

• Learn a mapping of visual feature 

descriptors to ground truth configuration 

space variables 

• Interpolate a parametric manifold and 

estimate unknown poses using only 

feature descriptors 

• Rotational accuracy in the range of 1 

degree 

• Robust to occlusions and small training 

set size 

• Applicable to closed-loop control as 

accurate feedback sensory information 

[Top] Sample images of a single 

segment of a continuum robot 

with an occluder. [Bottom] 

Algorithm flow 

08:55–09:00 WeAT9.6 

Label propagation in videos indoors with an 

incremental non-parametric model update 

Jorge Rituerto, Ana Cristina Murillo 

DIIS – i3A, Universidad de Zaragoza, Spain 

Jana Košecká 

Computer Science Department, George Mason University, USA 

• Towards semantic interpretation of the robot environment 

• Goal: label background regions and separate them from the 

“foreground” along robot acquired sequences. 

• Learning simple models from interest regions in first frame, then 

update and propagate along the sequence. 

• Ingredients: superpixel segmentation, description and EMD based 

correspondences. 

Summary of presented propagation process 

09:15–09:30 WeAT9.8 

Multilayer real-time video image stabilization 

• Real-time video image 

stabilization system (VISS) 

primarily developed for aerial 

robots 

• Four independent stabilization 

layers 

• Low-cost and robust 

• VISS significantly improves the 

stability of shaky video images 

Jens Windau, Laurent Itti 

University of Southern California, USA 


–112–

Session WeBT1 Continental Parlor 1 Wednesday, September 28, 2011, 10:00–11:30 

Socially Assistive Robots 

Chair Urbano Nunes, Univ. de Coimbra 

Co-Chair Eric Wade, Univ. of Southern California 

10:00–10:15 WeBT1.1 

Using Socially Assistive Robotics 

to Augment Motor Task Performance 

in Individuals Post-Stroke 

Eric Wade1 , Avinash Parnandi2 , and Maja J. Matarić1 1University of Southern California, Los Angeles, CA, USA 

2Texas A&M University, College Station, TX, USA 

• How can we influence performance measures using socially assistive 

robotics? 

10:30–10:45 WeBT1.3 

An Experience-Driven Robotic Assistant 

Acquiring Human Knowledge 

to Improve Haptic Cooperation 

Jose Ramon Medina, Martin Lawitzky, Alexander Mörtl, 

Dongheui Lee and Sandra Hirche 

Institute of Automatic Control Engineering, 

Technische Universität München, Germany 

Goal: 

Incremental learning of haptic 

primitives in complex scenarios 

during execution 

Proposed approach: 

• Combine incremental learning 

techniques with feedback 

control 

• Enhancement of autonomous 

segmentation, clustering, 

learning and execution 

performance by acquisition of 

meaningful semantic labels 

10:15–10:30 WeBT1.2 

The Impact of Different Competence Levels of 

Care-Receiving Robot on Children 

Madhumita Ghosh and Fumihide Tanaka 

Department of Intelligent Interaction Technologies, 

University of Tsukuba, Japan 

• New concept of robot use for education 

• Not like a teacher robot 

•But “Care-Receiving Robot (CRR)” which is 

taken care of (taught) by children 

• The goal is to achieve children’s 

spontaneous learning by teaching 

• Field trials at an English learning school 

for children were conducted 

• Investigation on two different types of CRR 

with presenting numerical data 

• CRR induced children’s behaviors which 

would lead to their learning reinforcement 

10:45–10:50 WeBT1.4 

Study on a Practical Robotic Follower 

to Support Home Oxygen Therapy Patients 

-Development and Control of a Mobile Platform- 

Atsushi Tani, Gen Endo, Edwardo F. Fukushima and Shigeo Hirose 

Dept. of Mechanical and Aerospace Engineering, Tokyo Institute of Technology, Japan 

Masatsugu Iribe 

The Faculty of Engineering, Osaka Electro-Communication University, Japan 

Toshio Takubo 

The First Department of Medicine, Tokyo Women’s Medical University, Japan 

• The patients suffering from severe lung 

diseases have to carry an oxygen tank 

when they go out. 

• We developed a low-cost mobile robot to 

carry the oxygen tank in an urban 

environment. 

• A tether interface is utilized to detect the 

relative position of the foregoing patient. 

• The robot can negotiate a curb separating a 

sidewalk from a street. 

• The following algorithm is also improved to 

track the patient’s trajectory. 

• We successfully demonstrated the following 

experiment in an outdoor environment. 


–113– 

Developed mobile robot to carry 

an oxygen tank


Socially Assistive Robots 

Chair Urbano Nunes, Univ. de Coimbra 

Co-Chair Eric Wade, Univ. of Southern California 

10:50–10:55 WeBT1.5 

Progress in Developing a Socially Assistive 

Mobile Home Robot Companion 

for Elderly with Mild Cognitive Impairment 

H.-M. Gross, C. Schroeter, S. Mueller, M. Volkhardt, E. Einhorn 

Neuroinformatics and Cognitive Robotics 

Ilmenau University of Technology, Germany 

A. Bley, Ch. Martin, T. Langner, M. Merten 

MetraLabs GmbH Ilmenau, Germany 

The paper presents 

� main system requirements derived from 

studies with the end-user target groups 

(elderly, relatives, caregivers) 

� consequences for the hardware design 

and functionality of the robot companion 

and its system architecture 

� a key technology for HRI in home 

environments – the autonomous user 

tracking and searching 

� results of already conducted and ongoing 

functionality tests with a particular focus 

on experimental results in autonomous 

user search 

89 years old lady in interaction with the 

mobile home robot companion developed in 

the European FP7 project CompanionAble 

11:00–11:15 WeBT1.7 

Whole-Body Contact Manipulation Using Tactile 

Information for the Nursing-Care Assistant Robot RIBA 

Toshiharu Mukai, Shinya Hirano, Morio Yoshida, and 

Hiromichi Nakashima 

RTC, RIKEN, Japan 

Shijie Guo 

SR Laboratorym Tokai Rubber Industries, Japan 

Yoshikazu Hayakawa 

Dept. of Mechanical Science and Engineering, Nagoya Univ., Japan 

• Whole-body contact manipulation 

method for nursing-care assistant robots 

in direct contact with patients is 

proposed. 

• This method preserves the conditions of 

surface contact, while manipulating the 

object’s position and orientation. 

• Information on the surface contact is 

obtained from tactile sensors mounted 

on the robot arms. 

• Results of basic experiments are also 

provided. 

10:55–11:00 WeBT1.6 

Wheelchair Navigation Assisted by Humanmachine 

Shared-control and P300-based Brain 

Computer Interface 

Ana C. Lopes, Gabriel Pires, Luís Vaz, and Urbano Nunes 

Institute for Systems and Robotics, University of Coimbra, Portugal 

• A Two-layer shared-control approach for 

assistive mobile robots, using a brain 

computer interface (BCI) is presented; 

• A P300-based paradigm that allows the 

selection of brain-actuated commands to 

steer the robotic wheelchair is proposed; 

• A virtual-constraint layer is responsible for 

validating user commands, based on 

context aware restrictions; 

• An intent-matching layer is responsible for 

determining the suitable steering 

command. 


–114– 

Overview of Robchair: ISR-UC 

robotic wheelchair platform 

11:15–11:30 WeBT1.8 

Should Robots or People Do These Jobs? 

A Survey of Robotics Experts and Non-Experts 

About Which Jobs Robots Should Do 

Wendy Ju and Leila Takayama 

Willow Garage, USA 

• This study identifies occupational predictors for which jobs robots could 

or should do. 

• Surveyed robot experts and non-experts (N=392, which includes 134 

robotics experts and 258 non-experts ) 

• Experts have more nuanced and guardedly optimistic view of robot 

capabilities. 

• Experts are more likely to focus on an occupation’s perceptual 

requirements in determining robot capability. 

• Non-experts are more skeptical about the robot’s ability to memorize or 

interact sociably.


Estimation & Sensor Fusion 

Chair Thierry Peynot, The Univ. of Sydney 

Co-Chair Agostino Martinelli, INRIA Grenoble-Rhone-Alpes 

10:00–10:15 WeBT2.1 

Vision-Aided Inertial Navigation: Closed-Form 

Determination of Scale, Speed and Attitude 

Agostino Martinelli, Chiara Troiani and Alessandro Renzaglia 

INRIA, Rhone Alpes, France 

• Algorithm based on a 

closed-form solution able to 

determine speed, attitude 

and absolute scale in a 

short time interval. 

• No prior knowledge or 

initialization required. 

• Only 4 camera poses and 

one single feature are 

necessary for the previous 

determination (or 2 features 

and 3 poses) 

• Implementation on a real 

quadrotor 

10:30–10:45 WeBT2.3 

Time-Varying Complementary Filtering for 

Attitude Estimation 

Evan Chang-Siu , Masayoshi Tomizuka 

Dept. of Mechanical Engineering, University of California Berkeley, USA 

Kyoungchul Kong 

Dept. of Mechanical Engineering, Sogang University, Korea 

• Robust method to estimate posture angle 

in a single degree of freedom rigid body. 

• Applies a complementary filter to a 

gyroscope and accelerometer for attitude 

estimation. 

• Adapts cut off frequency of 

complementary filter to achieve improved 

performance over a standard 

complementary filter. 

• Discusses stability and provides 

experimental results. 

Diagram of TVCF method 

10:15–10:30 WeBT2.2 

Attitude Determination Framework 

by Globally and Asymptotically Stable Bias Error Estimation 

with Disturbance Attenuation and Rejection 

Hideaki YAMATO and Takayuki FURUTA 

Future Robotics Technology Center, Chiba Institute of Technology, Japan 

Ken TOMIYAMA 

The Department of Advanced Robotics, Chiba Institute of Technology, Japan 

Attitude determination framework for costeffective 

and small-size attitude sensor 

units proposed, featuring 

• globally and asymptotically stable 

estimation of constant bias error of rate 

sensors by a quaternion feedback 

• disturbance attenuation as well as 

rejection by a disturbance evaluation 

scheme (existence and strength) and the 

conventional vector matching method 

• low computational effort suitable for costeffective 

motion sensor units 

• fundamental improvement of attitude 

error property as shown in the figure. 


–115– 

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Comparison of error properties: (a) by 

gyroscope sensors, (b) by accelerometers 

& magnetometers, and (c) by 

algorithm estimation proposed 

10:45–10:50 WeBT2.4 

A Sensor Fusion Approach to Angle and Angular 

Rate Estimation 

Daniel Kubus and Friedrich M. Wahl 

Institut fuer Robotik und Prozessinformatik, TU Braunschweig, Germany 

• Fusion of low-cost encoder and MEMS 

angular rate sensor measurements 

• Virtual increase of encoder resolution and 

elimination of rate sensor drift 

• Resolution gain of up to factor 10; 

outperforms standard Kalman filter 

• Easy to use, low computational 

requirements 

Sinusoidal trajectory. True angle, 

encoder output(dotted), and filter 

output(dashed).


Estimation & Sensor Fusion 

Chair Thierry Peynot, The Univ. of Sydney 

Co-Chair Agostino Martinelli, INRIA Grenoble-Rhone-Alpes 

10:50–10:55 WeBT2.5 

Combining Multiple Sensor Modalities for a 

Localisation Robust to Smoke 

Christopher Brunner, Thierry Peynot and Teresa Vidal-Calleja 

ACFR, The University of Sydney, Australia 

• Camera localisation robust to smoke/fog 

• Visible and IR cameras used within a 

SLAM framework 

• Robust localisation in smoke is shown: 

• using IR camera only 

• with Visual and IR cameras 

• with Visual and IR cameras plus 

visual data quality evaluation (visual 

images are discarded when most 

affected by smoke, mitigating errors) 

• Experimental validation with outdoor 

ground robot 

• Results show accuracy of proposed 

methods by comparing multiple 

trajectories with dGPS/INS ground truth 

11:00–11:15 WeBT2.7 

An Improved Pedestrian Inertial Navigation 

System for Indoor Environments 

Sylvie Lamy-Perbal, Mehdi Boukallel and Nadir Castañeda 

CEA List, Sensory and Ambient Interfaces Laboratory, France 

• An improved shoe-mounted inertial 

navigation system with submeter accuracy 

• Zero Velocity Update (ZUPT) and Angular 

update algorithms (AUPT) for drift 

compensation 

• Efficient foot stance phase detection 

system without additional sensors 

• In situ experimentation and validation 

10:55–11:00 WeBT2.6 

Multisensor Data Fusion for Robust Pose 

Estimation of a Six-Legged Walking Robot 

Annett Chilian, Heiko Hirschmüller, Martin Görner 

DLR German Aerospace Center, Germany 

• Fusion of IMU data, 3D visual odometry 

and 3D leg odometry using an indirect 

feedback information filter 

• Error estimate of visual odometry 

measurements is computed and 

considered in the filtering process 

• State cloning is applied to correctly fuse 

relative odometry measurements 

• Fusion result is robust against failures of 

the visual odometry caused by poor 

visual conditions 

• Experimental results prove the 

robustness and accuracy of the data 

fusion process 


–116– 

Fusion result compared to ground 

truth, visual and leg odometry under 

poor visual conditions 

11:15–11:30 WeBT2.8 

Learning the Delaunay Triangulation of 

Landmarks From a Distance Ordering Sensor 

Max Katsev and Steven M. LaValle 


University of Illinois at Urbana-Champaign, USA 

• Limited sensing: for any two landmarks, 

the robot can detect which one is closer 

• No other information: no exact distance, 

no landmark locations, no odometry 

• Algorithms for the robot to navigate to 

certain points and to learn global 

topological information about landmarks 

• Can be used to solve simple tasks, e.g., 

convex hull computation 

The environment is split into 

regions uniquely identified by the 

distance order of landmarks



Chair Wan Kyun Chung, POSTECH 

Co-Chair Sarthak Misra, Univ. of Twente 

10:00–10:15 WeBT3.1 

A Bimanual Teleoperated System for Endonasal 

Skull Base Surgery 

Jessica Burgner1 , Philip J. Swaney1 , D. Caleb Rucker1 , 

Hunter B. Gilbert1 , Scott T. Nill1 , Paul T. Russell III2 , 

Kyle D. Weaver3 , and Robert J. Webster III1,2 1 Department of Mechanical Engineering, Vanderbilt University, USA 

2 Department of Otolaryngology, Vanderbilt University Medical Center, USA 

3 Department of Neurosurgery, Vanderbilt University Medical Center, USA 

• Robotic surgery through a single 

nostril 

• “Tentacle-Like” concentric tube 

continuum robot manipulators 

• Presentation covers: 

- Surgical requirements 

- Workspace from patient data 

- Optimal end effector design 

- Actuation unit design 

- Jacobian-based teleoperation 

• Cadaver experiment results 

10:30–10:45 WeBT3.3 

Evaluation of Command Modes of an Assistance 

Robot for Middle Ear Surgery 

Guillaume Kazmitcheff1 , Mathieu Miroir1 , Yann Nguyen1 , Charlotte 

Célérier1 , Stéphane Mazalaigue3 , Evelyne Ferrary1,2 , Olivier 

Sterkers1,2 and Alexis Bozorg-Grayeli1,2 1 UMR-S 867 / Inserm / University Paris 7, France 

2 AP-HP Hôpital Beaujon, France 

3 Collin SA, France 

• Tele operated system : RobOtol 

• New prototype : improved controller system , mechanical enhancement 

and HMI concept 

• 2 command modes : Position-Velocity and Position-Position 

• Robot evaluation by tasks specific to middle ear surgery 

10:15–10:30 WeBT3.2 

Automated Surgical Planning and Evaluation 

Algorithm for Spinal Fusion Surgery with Three- 

Dimensional Pedicle Model 

Jongwon Lee1 , Sungmin Kim2 , Young Soo Kim3 , 

Wan Kyun Chung1 , and Minjun Kim1 1Department of Mechanical Engineering, POSTECH, KOREA 

2Department of Biomedical Engineering, Hanyang Univ., KOREA 

3Department of Neurosurgery, School of Medicine, Hanyang Univ., KOREA 

• Autonomous preoperative planning 

framework for lumbar spinal fusion was 

proposed 

• It augmented a novel functionality for 

suggesting optimal insertion trajectories 

of pedicle screws and its size (w.r.t. 

operational safety and screw-vertebra 

interface strength) 

• It is based on 3-D accurate spinal pedicle 

data segmented from patient’s CT images 

• It has been tested on 68 spinal pedicles 

of 8 patients, and successfully applied 

resulting in final safety margin of 

2.11�0.17mm 

5 

mm 

65 

70 

75 

80 


–117– 

mm 

20 

15 

10 

45 50 55 60 65 70 75 

mm 

Insertion trajectories of the pedicle 

screws computed by the proposed 

surgical planner 

10:45–10:50 WeBT3.4 

Towards validation of robotic surgery training 

assessment across training platforms 

Yixin Gao, Mert Sedef, Amod Jog, Peter Peng, 

Michael Choti, Gregory Hager, Jeff Berkley, Rajesh Kumar 

Computer Science, Johns Hopkins University, USA 

MIMIC Technologies, Inc. USA 

• Automated skill assessment in robotic 

surgery required for unsupervised 

feedback and simulated task 

performance 

• First cross-platform analysis of motion 

data to distinguish novice and 

proficient user performances 

• Experiments with da Vinci dry-lab and 

dV-Trainer simulation environment with 

users of varying skills are reported 

The prototype cross-platform 

architecture



Chair Wan Kyun Chung, POSTECH 

Co-Chair Sarthak Misra, Univ. of Twente 

10:50–10:55 WeBT3.5 

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11:00–11:15 WeBT3.7 

Mechanics of Needle-Tissue Interaction 

Roy J. Roesthuis, Youri R.J. van Veen, 

Alex Jahya and Sarthak Misra 


• Understanding the mechanics of 

needle-tissue interaction is required 

for pre-operative path planning and 

needle steering. 

• Needle-tissue interaction forces are 

identified for bevel-tipped needles 

inserted into a gelatin phantom. 

• The interaction forces are used to 

predict needle deflection using a 

mechanics-based model. 

• The average error between model 

and experiments for 100 mm 

insertion is 0.179 mm. 

Interaction forces on beveltipped 

needle during insertion 

Experimental setup to measure 

needle-tissue friction (top view) 

10:55–11:00 WeBT3.6 

An Analytical Model for Deflection of Flexible 

Needles During Needle Insertion 

Ali Asadian 1,2 , Mehrdad R. Kermani 1,2 , and Rajni V. Patel 1,2,3 

1 Canadian Surgical Technologies & Advanced Robotics (CSTAR), 

2 Dept. of Elect. & Comp. Eng., 3 Dept. of Surgery, 

University of Western Ontario, London, Ontario, Canada 

• The use of flexible needles in 

percutaneous interventions 

necessitates modeling of the 

generated curved trajectories. 

• A feasible model is essential 

for path planning (needle 

guidance) and simulation 

(clinician training). 

• Euler-Bernoulli beam theory 

and Green’s functions are 

used to obtain a needle’s 

planar deflection using. 

• The impact of tissue elasticity 

and needle-tissue friction is 

studied. 


–118– 

Effective insertion forces acting on the 

curved axis and the 2D needle deflection 

with respect to its insertion depth 

11:15–11:30 WeBT3.8 

Feasibility Study of an Optically Actuated 

MR-compatible Active Needle 

Seok Chang Ryu1 , Pierre Renaud2 , Richard J. Black3 , 

Bruce L. Daniel4 , and Mark R. Cutkosky1 1Center for Design Research, Stanford University, USA 

2LSIIT, Strasbourg University-CNRS-INSA, France 

3Intelligent Fiber Optic System Corporation, USA 

4Radiology, Stanford University, USA 

• We propose a novel MR-compatible, 

optically steerable active needle to 

improve needle maneuverability, which 

features an active flexure near tip for 

direct tip orientation control 

• The active flexure can be bent with an 

optically heated SMA wire by laser 

delivered through embedded optical 

fibers. It also includes optical curvature 

and temperature sensors 

Fig. Inner stylet of the proposed 

active biopsy needle 

• Mechanical behavior of the proposed needle was evaluated, and about 

10� of tip bending angle could be achieved 

• Optical heating requirements were investigated and side heating 

methods were proposed for better needle dynamics

Session WeBT4 Continental Ballroom 4 Wednesday, September 28, 2011, 10:00–11:30 

Symposium: Hardware and Software Design for Haptic Systems 

Chair Yasuyoshi Yokokohji, Kobe Univ. 

Co-Chair Katherine J. Kuchenbecker, Univ. of Pennsylvania 

10:00–10:15 WeBT4.1 

A Friction Differential and Cable 

Transmission Design for a 3-DOF Haptic 

Device with Spherical Kinematics 

Reuben Brewer 1 , Adam Leeper 1 , and J. Kenneth Salisbury 1,2,3 

Departments of Mech. Engineering 1 , Computer Science 2 , and Surgery 3 

Stanford University, USA 

• New design for a 3-DOF haptic 

device with spherical kinematics 

(pitch, yaw, and prismatic radial). 

• All motors grounded to reduce 

inertia and increase compactness 

near user’s hand. 

• Aluminum-aluminum friction 

differential actuates pitch and 

yaw robustly. 

• Cable transmission routes 

through the center of the 

differential to actuate the 

prismatic, radial DOF. 

10:30–10:45 WeBT4.3 

Coaxial Needle Insertion Assistant 

for Epidural Puncture 

Yoshihiko Koseki and Kiyoyuki Chinzei 

Advanced Industrial Science and Technology (AIST), Japan 

Danilo De Lorenzo 

Politecnico di Milano, Bioengineering Department, Italy 

Allison M. Okamura 

Department of Mechanical Engineering, Stanford University, USA 

• The coaxial needle separates cutting force 

at the needle tip from shear friction on the 

needle shaft 

• A robotic assist presents the cutting force 

in order to enhance physician’s perception 

during epidural puncture 

• The ratio of successful to unsuccessful 

puncture detection was higher with the 

assistant than without 

• Users were more confident that they could 

perceive the moment of puncture 

Operator 

Operator 

Force 

Inner Needle 

Outer Needle 

Friction Force 

Cutting Force 

Actuator 

Assistant 

Force 

Epidural Space 

Spinal Cord 

Lumbar Spine 

10:15–10:30 WeBT4.2 

Hi5: a versatile dual-wrist device to study 

human-human interaction and bimanual control 

Alejandro Melendez-Calderon, Lorenzo Bagutti, Brice Pedrono 

and Etienne Burdet 

Department of Bioengineering, Imperial College London, United Kingdom 

• Human-human and bimanual haptic 

interactions involve complex control 

requiring coordination of redundant 

muscle systems. 

• Wrist flexion/extension movements 

simplify the study of muscle control and 

redundancy. 

• Hi5 allows to measure the kinematics, 

dynamics and muscular activity, allowing 

systematic analysis of redundant 

strategies. 

• The simple design enables quick and 

easy reconfiguration of the interface to 

investigate symmetric or asymmetric 

tasks on both human-human or 

bimanual interaction experiments. 


–119– 

Removable carbon fiber tube 

Adjustable position 

Human-human Bimanual 

10:45–10:50 WeBT4.4 

Two-Hands Are Better Than One: Assisting 

Users with Multi-Robot Manipulation Tasks 

Bennie Lewis and Gita Sukthankar 

Department of EECS, University of Central Florida, USA 

• User interface for reducing operator 

workload while controlling a team of 

robots with manipulators. 

• Robots leverage information from 

commands that the user has executed in 

the past. 

• Two new modes which can be invoked by 

the user or autonomously by the interface. 

• Shows statistically-significant 

improvements in the time required to 

perform foraging scenarios


Symposium: Hardware and Software Design for Haptic Systems 

Chair Yasuyoshi Yokokohji, Kobe Univ. 

Co-Chair Katherine J. Kuchenbecker, Univ. of Pennsylvania 

10:50–10:55 WeBT4.5 

3-DOF Haptic Feedback for Assisted 

Driving of an Omnidirectional Wheelchair 

Quinton M. Christensen and Stephen Mascaro 


• The mobility limitations of 

conventional wheelchairs have 

prompted development of 

omnidirectional wheelchairs. 

• The 3-DOF Hapic Joystick shown 

provides a natural and intuitive 

controller for these wheelchairs. 

• This joystick provides force feedback 

corresponding to all degrees of 

control input. 

• This controller can assist in obstacle 

avoidance and driver training and 

has other research applications. 

Functional Prototype of the 

3-DOF Haptic Joystick 

11:00–11:15 WeBT4.7 

Asynchronous Haptic Simulation of Contacting 

Deformable Objects with Variable Stiffness 

Igor Peterlik, Christian Duriez, Stephane Cotin 

INRIA Lille Nord Europe, France 

• In complex simulations rapid variation of 

force can be introduced due to stiff nonlinear 

deformations 

• We present asynchronous technique for 

constraint-based haptic rendering of 

deformable or rigid objects in contact 

• The method is demonstrated with two 

deformable objects (volumetric and 

curved), each having different stiffness, 

simulated at different frequencies 

• The experiments shows that our 

multirate method results in stable, 

coherent and precise haptic rendering of 

contacts between the objects simulated 

asynchronously 

Volumic object simulated at low 

frequency (50 Hz), curved 

object (snap, thread) simulated 

at high frequncy (1000 Hz) 

10:55–11:00 WeBT4.6 

Configuration-based Optimization for Six 

Degree-of-Freedom Haptic Rendering 

Using Sphere-trees 

Xin Zhang, Dangxiao Wang and Yuru Zhang 

the State Key Lab of Virtual Reality Technology and Systems 

Beihang University, China 

Jing Xiao 

The Department of Computer Science, 

University of North Carolina – Charlotte, USA 

• Treated the haptic interaction between a 

rigid tool and rigid objects in a virtual 

environment as a constrained optimization 

problem in the 6-D configuration space of 

the tool. 

• Modeled the tool and objects as sphere 

trees for fast detection of multiple 

collisions and formulation of contact 

constraints. 

• Applied the approach to dental surgery 

simulation. Results show stable interaction 

in the update rate of 1kHz. 


–120– 

Six DoF haptic rendering for 

dental surgery simulation 

11:15–11:30 WeBT4.8 

Proxy Method for Fast Haptic Rendering from 

Time Varying Point Clouds 

Fredrik Rydén, Sina Nia Kosari and Howard Jay Chizeck 

Department of Electrical Engineering, University of Washington, USA. 

• A novel algorithm for haptic rendering from 

time varying point clouds in real time. 

• The algorithm can run on a regular laptop 

(Core 2 Duo or better) and successfully 

render haptic forces at 1000 Hz. 

• The user can easily feel features that are 

less than 5 mm. 

• Algorithm is based on the extension of the 

haptic proxy idea to dynamic point clouds. 

• Potential application in gaming and telerobotics 

applications including robotic 

assisted surgery. 

The algorithm running on a 

regular laptop. The size of the 

proxy is an important parameter 

in haptic interaction and can here 

be compared to an apple.


Symposium: Foundations and Future Prospects of Sampling-based Motion Planning 

Chair Kostas E. Bekris, Univ. of Nevada, Reno 

Co-Chair Steven M LaValle, Univ. of Illinois 

10:00–10:15 WeBT5.1* 

Semi-Plenary Invited Talk: Sampling-based Algorithms 

for General Motion Control Problems: Recent 

Advances and New Challenges 

Emilio Frazzoli, Massachusetts Institute of Technology 

10:30–10:45 WeBT5.3 

An Obstacle-responsive Technique 

for the Management and Distribution 

of Local Rapidly-exploring Random Trees 

Nathan A. Wedge and Michael S. Branicky 


Case Western Reserve University, Cleveland, Ohio, USA 

• Previously-introduced PART (Path-length 

Annexed Random Tree) builds on RRT by 

addressing pathologically-difficult cases 

• PART adds threshold on path length to 

partition search into local trees, providing 

multiple neighbors for diverse expansion 

• APART (Adaptive PART) supplements 

PART by adjusting thresholds for each 

local tree based on obstacles 

• Adaptive mechanism focuses effort near 

presumed difficult regions and improves 

performance over RRT and PART on hard 

problems via apt threshold values 

APART’s search focuses on tricky 

areas like narrow obstacle edges 

10:15–10:30 WeBT5.2 

Semi-Plenary Invited Talk: Sampling-based Algorithms 

for General Motion Control Problems: Recent 

Advances and New Challenges 

Emilio Frazzoli, Massachusetts Institute of Technology 

10:45–10:50 WeBT5.4 

Finding Critical Changes in 

Dynamic Configuration Spaces 

Yanyan Lu and Jyh-Ming Lien 

Department of Computer Science, George Mason University, USA 

• Probabilistic methods plan in 

configuration-time space and do not 

reuse computation between 

configuration space slices. 

• There exist methods reuse the valid 

portion of roadmap but only update 

the roadmap at fixed times. 

• Our method identifies critical changes 

in the configuration space and reuses 

valid edges and nodes, and updates 

roadmap only at the critical times. 

• Our planner provides better 

completeness and is at least 10 times 

faster than existing popular planners. 


–121– 

Topological changes of configuration 

free space from t0 to t2


Symposium: Foundations and Future Prospects of Sampling-based Motion Planning 

Chair Kostas E. Bekris, Univ. of Nevada, Reno 

Co-Chair Steven M LaValle, Univ. of Illinois 

10:50–10:55 WeBT5.5 

Toggle PRM: 

A Simultaneous Mapping of C free and C obstacle 

-A Study in 2D- 

Jory Denny and Nancy M. Amato 

Computer Science and Engineering, Texas A&M University, USA 

• New paradigm for PRM samplingbased 

planner 

• A simultaneous, coordinated mapping 

of both C free and C obst 

• Theoretical and experimental 

improvement for sampling in narrow 

passages 

• Improved efficiency in terms of number 

of validity checks 

Uniform OBPRM 

Toggle PRM Free Toggle PRM Block 

-Example map constructions- Toggle PRM 

has highest density in the passage 

11:00–11:15 WeBT5.7 

EG-RRT: Environment-Guided Random Trees 

for Kinodynamic Motion Planning 

with Uncertainty and Obstacles 

Léonard Jaillet*, Judy Hoffman+, Jur van den Bergº, 

Pieter Abbeel+, Josep M. Porta* and Ken Goldberg+ 

* Institut de Robòtica i Informàtica Industrial, Barcelona, Spain 

+ University of California, Berkeley, USA 

º University of North Carolina, Chapel Hill, USA 

• EG-RRT is an Environment-Guided 

variant of RRT designed for 

kinodynamic systems 

• This hybrid planner integrates the 

local limitations of the system 

dynamics as well as the risk of leading 

to unproductive expansions 

• The method may be enhanced by a 

cost model based on the LQG-MP 

framework 

• It produces paths faster and with a 

lower probability of collision under 

uncertainty in control and sensing 

Comparison of four RRT-based 

planners. EG-RRT in the lower-right 

outperforms the other variants 

10:55–11:00 WeBT5.6 

Sampling Heuristics for Optimal Motion 

Planning in High Dimensions 

Baris Akgun and Mike Stilmand 

The School of Interactive Computing, Georgia Institute of Technology, USA 

• Scenario: Allocated time for planning 

• Find an initial path quickly and iteratively 

decrease its cost fast 

• Introduce heuristics to RRT* algorithm to 

balance exploration and exploitation 

• Implement a bidirectional version for 

efficiency in high-dimensions 

• Local Biasing: Exploitation 

• Node rejection: Efficiency 


–122– 

Trees produced in same duration 

Top: With plain RRT* 

Bottom: With combined heuristics 

11:15–11:30 WeBT5.8 

Planning Humanlike Actions in Blending Spaces 

Yazhou Huang, Mentar Mahmudi 

and Marcelo Kallmann 

School of Engineering, University of California Merced, USA 

• We introduce an approach for enabling 

sampling-based planners to compute 

motions with humanlike appearance. 

• The search space of our planner is 

defined on a space of blendable motion 

capture examples, generating solutions 

similar to the original mocap examples. 

• The method is combined with a 

locomotion planner in order to generate 

whole-body motions for generic action 

execution among obstacles.


Symposium: Aerial Robotics: Control and Planning 

Chair Srikanth Saripalli, Arizona State Univ. 


10:00–10:15 WeBT6.1 

UAV Rotorcraft in Compliant Contact: 

Stability Analysis and Simulation 

Paul Pounds and Aaron Dollar 


• End-effector compliance can allow for safe 

interactions of a hovering vehicle with 

objects, without structural contact 

knowledge 

• The 6-DOF dynamics of a helicopter with 

nonlinear PD control remain stable for a 

range of end-effector stiffnesses and 

displacements from aircraft center of 

gravity 

• Simulation of the system for archetypical 

end-effector configurations demonstrate 

stability performance predicted analytically 

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Unforced position 

Helicopter coupled to ground 

via compliance 

10:30–10:45 WeBT6.3 

Flight Control for Target Seeking by 

13 gram Ornithopter 

Stanley S. Baek, Fernando L. Garcia Bermudez, 

and Ronald S. Fearing 

Dept. of EECS, University of California, Berkeley, United States 

• Autonomous flight control of a 13 gram 

ornithopter flying toward a target using 

onboard sensing and computation only 

• 1.0 gram control electronics including a 

microcontroller, inertial and visual 

sensors, wireless communication, and 

motor drivers. 

• Simplified aerodynamic model of 

ornithopter flight to reduce the order of 

the control system. 

• Dead-reckoning algorithm to recover 

from temporary loss of the target. 

• Landed within a radius of 0.5 m from the 

target with more than 85% success (N = 

20) 

target 



10:15–10:30 WeBT6.2 

Design, Modeling, Estimation and Control 

for Aerial Grasping and Manipulation 

Daniel Mellinger, Quentin Lindsey, 

Michael Shomin, and Vijay Kumar 

GRASP Lab, University of Pennsylvania, USA 

• We present the design of several grippers 

that allow quadrotors to pick up and 

transport payloads 

• We describe methods for estimating and 

adapting to the changes in system 

dynamics due to the grasped payloads 

• We present experimental results for the 

estimation methods and show 

considerable improvement in tracking 

performance with the inclusion of the 

estimated parameters 


–123– 

Quadrotors Tranporting Payloads 

10:45–10:50 WeBT6.4 

Embedded robust nonlinear control for a four-rotor 

rotorcraft: Validation in real-time with wind gusts 

Laura E. Muñoz, Pedro Castillo, Guillaume Sanahuja 

Heudiasyc UMR CNRS 6599, UTC France 

Omar Santos 

CITIS - Universidad Autónoma del Estado de Hidalgo, México 

• An embedded robust nonlinear controller to stabilize a fourrotor 

rotorcraft in presence of bounded disturbances is 

proposed. 

• The stability analysis of the close-loop system is proved 

using the Lyapunov theory. 

• A new prototype with an embedded control system has been 

developed. 

• The proposed algorithms were validated through simulation 

and real time experiments.


Symposium: Aerial Robotics: Control and Planning 

Chair Srikanth Saripalli, Arizona State Univ. 


10:50–10:55 WeBT6.5 

Differential Flatness Based Control of a 

Rotorcraft For Aggressive Maneuvers 

Jeff Ferrin, Robert Leishman and Tim McLain 

Mechanical Engineering, Brigham Young University, U.S.A. 

Randy Beard 

Electrical Engineering, Brigham Young University, U.S.A. 

• A differential flatness based control 

method for rotorcraft is presented 

• Feed forward term allows for improved 

path following performance with large 

pitch and roll angles 

• The control method also allows for 

independent yaw control while flying the 

path 

• Hardware results demonstrate substantial 

improvement over standard feedback 

control methods (no feed forward term) 

Pitch and roll angles of aircraft 

during aggressive flight 

11:00–11:15 WeBT6.7 

Magnetic Localization for 

Perching UAVs on Powerlines 

Joseph Moore and Russ Tedrake 

Computer Science and Artificial Intelligence Lab (CSAIL), MIT, USA 

• Goal: Perch on powerlines to recharge 

• Potential challenges 

• Unknown current phase leads to 

ambiguity in positions 

• Signal strength decreases like 

1/distance^3 

• Developed onboard magnetometer and 

estimation algorithm (using EKF) 

• Capable of producing sufficient estimates 

for our dynamic perching maneuvers, 

which start 4m from wire at 8m/s. 

10:55–11:00 WeBT6.6 

Robust Embedded Egomotion Estimation 

Rainer Voigt, Janosch Nikolic, Christoph Hürzeler, 

Stephan Weiss, Laurent Kneip and Roland Siegwart 

Autonomous Systems Lab, ETH Zürich, Switzerland 

• Robust and efficient egomotion estimation in 

challenging industrial environments 

• Tight coupling of visual and inertial cues to 

cope with poorly or repetitively textured 

environments 

• Real-time operation on low-power embedded 

computing platform 

11:15–11:30 WeBT6.8 

Persistent Surveillance with a Team of MAVs 

Nathan Michael and Kartik Mohta 


Ethan Stump 

Army Research Laboratory, USA 

• Consider the problem of deploying a team 

of aerial robots to persistently survey 

discrete locations of interest 

• Formulate the problem as a Vehicle 

Routing Problem with Time Windows and 

detail a real-time optimal solution 

methodology 

• Detail a system architecture capable of 

addressing the problem of persistent 

surveillance with a team of autonomous 

micro-aerial vehicles 

• Report simulation and experimental 

results for teams of quadrotors both 

indoors and outdoors 


–124– 

Two quadrotors are deployed 

autonomously to survey discrete 

interest points


Passive Walking & Leg-Wheeled Robots 

Chair Dong-Soo Kwon, KAIST 

Co-Chair Martin Buehler, iRobot 

10:00–10:15 WeBT7.1 

Nonlinear Structure of Escape-Times to Falls for 

a Passive Dynamic Walker in an Irregular Slope 

(A, B, C) (A) 

Hiromichi Suetani and Aiko Ideta 

(A) Department of Physics and Astronomy, Kagoshima University, Japan 

(B) Decoding and Controlling Brain Information, PRESTO, JST, Japan 

(C) RIKEN Advanced Science Institute, Japan 

(D) 

Jun Morimoto 

(D) 

Dept. of BRI, ATR Computational Neuroscience Laboratories, Japan 

• Passive dynamic walkers (PDWs) use the 

“limit cycle attractors” for their gait. But, 

the area of their basins is very small, i.e., 

PDW is very susceptible to noise. 

• A set of escape-times from walking to 

falling of PDW with noise exhibits a 

characteristic nonlinear structure in the 

state space, which can be considered as 

the “basin ruin” of the noiseless PDW. 

• Analyzing such a nonlinear structure 

using a multi-class Support Vector 

Machines and Canonical Correlation 

Analysis 

• Nonlinear structure of this escape-times 

may be useful for reinforcement 

learning of PDWs. 

Escape-times from walking to 

falling in a Poincare surface. 

Each color indicates the number 

of steps left before falling. 

10:30–10:45 WeBT7.3 

Optimal gait switching for legged locomotion 

B. Kersbergen, G.A.D. Lopes, 

T.J.J. van den Boom, B. De Schutter and R. Babuska 

Delft Center for Systems and Control, 

Delft University of Technology, The Netherlands 

• The gait space of multi-legged robots is 

combinatorial 

• We use switching max-plus linear models 

to describe legged locomotion 

• We present a systematic way to generate 

optimal gait transitions, in the sense of 

minimizing the foot velocity variation 

during stance 

• All gait switches are intrinsically safe 

Optimal gait switch for an 

hexapod robot 

10:15–10:30 WeBT7.2 

Increasing the Robustness of Acrobot walking 

control using compliant mechanisms 

Maziar Ahmad Sharbafi and Mohammad Javad Yazdanpanah 

and Majid Nili Ahmadabadi 

School of Electrical and Computer Engineering, University of Tehran, Iran 

• Two controllers are designed based on 

Hybrid Zero Dynamics for flat and inclined 

surfaces. 

• Calculating the difference of force 

generated by two controller on inclined 

surface 

• Finding a nonlinear compliance to 

compensate the required force 

• Evaluation of the controller on flat surface 

• Evaluation of the controller on inclined 

surface 

• Analyzing the controller performance 


–125– 

Walking on slope with HZD based 

designed controller for flat surface. 

a nonlinear compliance is inserted 

to enable it to walk on slope. 

10:45–10:50 WeBT7.4 

Development and Experiment of a Kneed Biped Walking 

Robot Based on Parametric Excitation Principle 

Yoshihisa Banno, Kouichi Taji and Yoji Uno 

Mechanical Science and Engineering, Nagoya University, Japan 

Yuji Harata 

Mechanical Systems and Applied Mechanics, Hiroshima University, Japan 

• The parametric excitation walking with a 

simple periodic reference trajectory was 

realized. 

• A kneed biped robot based on parametric 

excitation principle were developed. 

• In the experiment, a sustainable walking 

on a level ground was achieved about 17 

steps. 

• Several angular coordinates of 

experimental results were very close to 

those of simulation results. 

Developed biped robot


Passive Walking & Leg-Wheeled Robots 

Chair Dong-Soo Kwon, KAIST 

Co-Chair Martin Buehler, iRobot 

10:50–10:55 WeBT7.5 

Switchblade: An Agile Treaded Rover 

Nicholas Morozovsky, Christopher Schmidt-Wetekam, 

Thomas Bewley 

Mechanical and Aerospace Engineering Department, University of California 

San Diego, USA 

• Able to traverse rough terrain while 

retaining a small form factor for navigating 

confined spaces 

• Combination of a novel transforming 

mechanical design, capable onboard 

electronics, and advanced feedback 

control algorithms 

• Manipulates its center of mass to perform 

unique maneuvers 

• Well suited for a variety of sociallyrelevant 

applications, including 

reconnaissance, mine exploration, and 

search & rescue 

11:00–11:15 WeBT7.7 

Robust obstacle crossing of a wheel-legged 

mobile robot using minimax force distribution 

and self-reconfiguration 

Pierre Jarrault, Christophe Grand and Philippe Bidaud 

Institut des Systèmes Intelligent et de Robotique 

Université Pierre et Marie Curie, France 

• Control algorithm for obstacle 

crossing with a poly-articulated 

wheeled mobile robot 

• Stability criterion based on the 

smallest force sustainable at the 

wheel/ground contact 

• Optimization of both the force 

distribution and the center of mass 

location in order to maximize the 

criterion 

• Cross-ability analysis of an obstacle 

10:55–11:00 WeBT7.6 

Passive Dynamic Walking of Combined Rimless 

Wheel and Its Speeding-up by Adjustment of 

Phase Difference 

Ryosuke Inoue, Fumihiko Asano, Daiki Tanaka and Isao Tokuda 

School of Infromation Science, 

Japan Advanced Institute of Science and Technology, Japan 

• A combined rimless wheel (CRW) that 

consists of two identical 8-legged rimless 

wheels is introduced. 

• Numerical simulations show that the 

walking speed dramatically increases by 

adjustment of the phase difference. 

• The validity of the theoretical results is 

confirmed by using a prototype 

experimental CRW. 

• The mechanism of speeding-up is 

investigated from the potential barrier 

point of view. 

11:15–11:30 WeBT7.8 

Zero-Moment Point Feedforward Balance 

Control of Leg-Wheel Hybrid Structures by using 

Input/Output Linearization 

Sang-ik An and Dong-Soo Kwon 

Mechanical Engineering, KAIST, Korea 

• A balance control algorithm is proposed 

which maximizes the dynamic stability by 

controlling the ZMP to remains at the 

geometric center of the supporting 

polygon while the robot traverses the 

desired trajectory. 

• The ZMP feedforward constraint is 

inserted into the dynamically decoupled 

model to satisfy the maximum dynamic 

stability, but it generates the internal 

dynamics. 

• Input/Output Linearization is utilized to 

address the problem of the internal 

dynamics. 


–126– 

Leg-Wheel Hybrid Robot


Multirobot Systems: Rendezvous & Task Switching 

Chair Richard Vaughan, Simon Fraser Univ. 

Co-Chair Filippo Arrichiello, Univ. di Cassino 

10:00–10:15 WeBT8.1 

Bayesian Rendezvous for Mobile Robots 

Sven Gowal and Alcherio Martinoli 

DISAL, EPFL, Switzerland 

• We study the rendezvous with differentialwheeled 

robots. 

• Robots have a limited and noisy 

perception of their neighboring 

teammates. 

• Rendezvous convergence properties are 

proven under noisy observations. 

• A Bayesian framework is exploited to 

improve the rendezvous performance. 

• Experiments are carried out on real 

Khepera III robots equipped with a IRbased 

range and bearing module. 

3 simulated Khepera III robots 

performing the Bayesian 

rendezvous with a particle filter. 

10:30–10:45 WeBT8.3 

A decentralized controller-observer scheme 

for multi-robot weighted centroid tracking 

Gianluca Antonelli, Filippo Arrichiello, Alessandro Marino 

DAEIMI, University of Cassino, Italy 

Fabrizio Caccavale 

DIFA, University of Basilicata, Italy 

• The paper presents a decentralized 

controller-observer scheme for centroid 

tracking with a multi-robot system 

• Each robot uses information from a local 

observer to cooperatively track an 

assigned time-varying reference 

• Convergence of the scheme is proven for 

fixed and switching communication 

topologies, and for directed and 

undirected graphs 

• Numerical simulations relative to different 

case studies are illustrated 

10:15–10:30 WeBT8.2 

Power-Aware Rendezvous with Shrinking 

Footprints 

Hassan Jaleel and Magnus Egerstedt 

Electrical and Computer Engineering 


• Power consumption is linked to sensor 

footprint area in multi-agent networks. 

• The rendezvous problem is studied when 

the footprints shrink over time due to 

power consumption. 

• A nonlinear interaction law is proposed for 

ensuring that no interactions are lost 

despite the shrinking footprints. 

• Lyapunov-based techniques are used to 

ensure that connectivity is achieved for all 

times. 

10:45–10:50 WeBT8.4 

Market-based Coordination of 

Coupled Robot Systems 

Ling Xu and Anthony Stentz 


• We address the problem of 

environmental coverage with multiple 

robots 

• To solve this coupled problem, we 

introduce a parallel branch-and-bound 

approach that divides the computation 

among the robots 

• We also introduce an auctions 

framework, based on market-based 

techniques, which enables the robots to 

share the work and focus the search 

• Test results show parallel branch-andbound 

with auctions can lead to 

significant improvements for both search 

tree size and computation time 


–127– 

Parallel branch-and-bound with 

auctions


Multirobot Systems: Rendezvous & Task Switching 

Chair Richard Vaughan, Simon Fraser Univ. 

Co-Chair Filippo Arrichiello, Univ. di Cassino 

10:50–10:55 WeBT8.5 

Event-driven Gaussian Process for Object 

Localization in Wireless Sensor Networks 

Jae Hyun YOO, Woojin Kim, and H.Jin KIM 

School of Mechanical & Aerospace Engineering, 

Seoul National University, South Korea 

• Wireless sensor networks suffer from 

excessive traffic between communicating 

nodes 

• In error-bounded algorithm, each node 

decides whether or not to transmit data 

to a sink node 

• Gaussian process regression is 

employed for position estimation of a 

unknown object 

• RSSI(Received Signal Strength 

Indicator), IR(Infrared Rays) and 

Magnetic sensors are used 

• Experimental results demonstrate the 

efficiency and accuracy of the proposed 

event-driven Gaussian process approach 

Experimental setup for the 

evaluation of the event-driven 

Gaussian process with thirty sensor 

nodes and one mobile robot 

11:00–11:15 WeBT8.7 

Task Switching in Multirobot Learning 

through Indirect Encoding 

David D’Ambrosio and Joel Lehman and Sebastian Risi and 

Kenneth Stanley 


University of Central Florida, United States 

• Training multiple robots to work together 

to perform complementary tasks is difficult 

• In this paper, multiagent HyperNEAT 

exploits the regularities among different 

robots and different tasks 

• Teams of Khepera III robots cooperatively 

explore an area and then return home 

when called 

• Teams that exploit regularities between 

subtasks solve the problem more 

consistently than those that cannot 

Z 

Multiagent HyperNEAT exploits 

the relationship among robots 

10:55–11:00 WeBT8.6 

Multi-Robot Patrolling with Coordinated 

Behaviours in Realistic Environments 

Luca Iocchi 1 , Luca Marchetti 1,2 , Daniele Nardi 1 

1 Department of Computer and Systems Science,\newline 

Sapienza, University of Rome, Italy. 

2 INRIA Sophia Antipolis - Mèditerranèe 

Sophia Antipolis, France. 

• Multi-Robot Patrolling needs more experiments in real and realistic 

simulated environments 

• Many strategies (devised and tested on abstract simulators) may act suboptimally 

or fail in real environments. 

• MRP needs on-line coordination in order to deal with un-modeled aspects 

of the problem during execution. 


–128– 

Contributions 

• Realistic simulator (ROS) 

• Evaluation of standard MRP 

strategies under real conditions. 

• Definition of coordinated behaviors 

to improve MRP strategies in real 

environments. 

11:15–11:30 WeBT8.8 

Multi-Robot Coordination Methodology 

in 

Congested Systems with Bottlenecks 

Satoshi Hoshino 

Chemical Resources Laboratory, Tokyo Institute of Technology, Japan 

• Novel coordination methodology for 

autonomous mobile robots in congested 

systems with bottlenecks is proposed 

• Robot behavior control technique is 

improved to generate more efficient 

interaction force among robots 

• Environmental rule is designed to exert 

amplified interaction force on robots 

moving in congestion 

• Effectiveness of the robot behavior 

control technique and environmental rule 

through simulation experiments is shown 

Entrance 

1 

2 

Circuit 1 

8 

7 

Moving direction 

Velocity [m/s] 

1.5 

1.0 

0.5 Increased velocity 

0 

Constant & safe 

length L = 3.0 [m] 

3 

Crossing: C 1 & C 2 

Junction: C 1 & C 3 

Entrance 

9 

10 

Circuit 3 Circuit 2 

16 

6 5 

Crossing: C 1 & C 2 Lane unit = 20 [m] 

Junction: C 2 & C 3 

15 

40 [m] 

14 

13 

C 1 

Crossing & junction 

Simulation environment and 

velocity distribution in each lane 

C 3 

4 

C 2 

11 

12 

Original velocity



Chair Francois Chaumette, INRIA Rennes-Bretagne Atlantique 

Co-Chair Seth Hutchinson, Univ. of Illinois 

10:00–10:15 WeBT9.1 

From Optimal Planning to Visual Servoing 

With Limited FOV 

Paolo Salaris, Lucia Pallottino and Antonio Bicchi 

Interdept. Research Center “Enrico Piaggio”, University of Pisa, Italy 

Seth Hutchinson 

Dept. of Electrical and Computer Engineering, University of Illinois, USA 

• This paper presents an optimal feedback 

control scheme to drive a vehicle toward a 

desired position 

• The vehicle is equipped with limit Field-Of- 

View camera and must follow the shortest 

path keeping a given landmark in sight 

• Feedback control laws are defined 

exploiting geometric properties of the 

synthesis available from previous works 

Example of an optimal (shortest) 

path on the motion plane 

• By using a slightly generalized stability analysis setting, which is that 

of stability on a manifold, a proof of stability is given 

• Simulations and experiments demonstrate the effectiveness of the 

proposed technique 

10:30–10:45 WeBT9.3 

Intensity-based visual servoing for non-rigid 

motion compensation of soft tissue structures 

due to physiological motion using 4D ultrasound 

Deukhee Lee and Alexandre Krupa 

INRIA Rennes-Bretagne Atlantique, IRISA, France 

• Intensity-based visual tracking of 3D nonrigid 

motion in real time 

− 3D non-rigid motion model using 

TPS 

− Intensity-based motion estimation of 

a set of 3D points 

• Visual servo control structure to stabilize 

3D non-rigid motion using 4D-US imaging 

− Global rigid motion extraction 

− Position-based visual servo control 

• Successful robotic experiments for motion 

compensation with deformable TM 

phantom 

Manipulator 

6-DOF robot 

4D ultrasound ound probe 

Abdominal US 

phantom 

Turning table 

Drawer 

Deformable bl phantom h t 

A 6-DOF robot holding a 4D-US 

probe compensates rigid 

motion(top) and non-rigid 

motion(bottom). 

10:15–10:30 WeBT9.2 

Constrained Manipulator Visual Servoing (CMVS): 

Rapid Robot Programming in Cluttered Workspaces 

Ambrose Chan and Elizabeth Croft 


James Little 

Computer Science, University of British Columbia, Canada 

• Model-free visual servoing of eye-in-hand 

manipulators in cluttered environments 

• Exploits the natural by-products of the 

teach-by-showing process, to help the 

robot navigate non-convex spaces 

• Robot servos to previously untaught 

locations under challenging constraints: 

• whole-arm collisions 

• object occlusions 

• robot's joint + camera's sensing limits 

• Automatic extraction of relevant cost 

functions and constraints for servoing 

• Experimental verification with Barrett WAM 

7-DOF + Sony XC-HR70 camera 


–129– 

Robot observes while the user 

obtains a “reference image” for 

visual servoing 

10:45–10:50 WeBT9.4 

Improving ultrasound intensity-based visual 

servoing: tracking and positioning tasks with 

2D and bi-plane probes 

Caroline Nadeau and Alexandre Krupa 

IRISA, INRIA Rennes-Bretagne Atlantique, France 

• Control of the in-plane and out-of-plane 

motions of an ultrasound probe 

• Intensity-based approach (no image 

processing or segmentation step) 

• On-line estimation of the current 

interaction matrix for positioning task with 

a classical 2D probe 

• Modelling of a bi-plane approach and 

experimental results with 2D and bi-plane 

ultrasound probes 

Validation of the method with a 

robotic arm using a realistic 

abdominal phantom



Chair Francois Chaumette, INRIA Rennes-Bretagne Atlantique 

Co-Chair Seth Hutchinson, Univ. of Illinois 

10:50–10:55 WeBT9.5 

Automatic landing on aircraft carrier by 

visual servoing 

Laurent Coutard and François Chaumette 

INRIA Rennes-Bretagne Atlantique and IRISA, France 

Jean-Michel Pflimlin 

Dassault Aviation, France 

• Alignment and landing tasks toward a 

moving aircraft carrier 

• 2D visual features, inspired by cues 

used by pilots, are related to aircraft 

state 

• Control with outputs feedback: 

•2D visual features 

•Inertial Central Unit 

• Validation with a 3D model-based tracker 

in a high fidelity simulator 

11:00–11:15 WeBT9.7 

Towards Vision-Based Control of Cable-Driven 

Parallel Robots 

Tej Dallej, Marc Gouttefarde, Nicolas Andreff, Micaël Michelin 

and Philippe Martinet 

(LASMEA/U.B.P - LIRMM - TECNALIA), FRANCE 

• Visual servoing methods are a good 

alternative for the control of cable-driven 

parallel robots. 

• The end-effector pose is measured using 

a camera and used for regulation. 

• The use of computer vision in the 

feedback simplifies the kinematic 

modeling. 

• The forward kinematic model is never 

used in the control. 

A photograph of ReelAx8 mobile 

platform observed by a camera. 

10:55–11:00 WeBT9.6 

Combining IBVS and PBVS to ensure the 

visibility constraint 

Olivier Kermorgant and François Chaumette 

Lagadic, INRIA Rennes-Bretagne Atlantique, France 

• Hybrid visual servoing based on a generic fusion approach 

• Pure PBVS is performed inside a safe image area 

• 2D information is added to keep the object in the image 

• Validation with simulation and experiments 


–130– 

Visual servo with a 3D tracking 

The model nodes are used as 2D 

features when they approach the 

image border 

11:15–11:30 WeBT9.8 

Time-Analysis of a Real-Time Sensor-Servoing 

System using Line-of-Sight Path Tracking 

Johannes Schrimpf 1 , Morten Lind 2 and Geir Mathisen 1,3 

1 Dept. of Engineering Cybernetics, 

Norwegian University of Science and Technology, Norway 

2 Dept. of Production and Quality Engineering, 

Norwegian University of Science and Technology, Norway 

3 Dept. of Applied Cybernetics, SINTEF ICT, Norway 

• Case system: Visual servoing 

for path-tracking with a real-time 

controlled robot. 

• Measurement and estimation of 

the delays in the control loop. 

• Stability analysis of a simple 

control-model based on pure 

delays in the control loop. 

• Qualitative validation of the 

estimated delay by simulated 

and experimental data.

Session WeCT1 Continental Parlor 1 Wednesday, September 28, 2011, 14:00–15:30 

Human-Robot Interaction and Cooperation 

Chair Weihua Sheng, Oklahoma State Univ. 

Co-Chair Rachid Alami, CNRS 

14:00–14:15 WeCT1.1 

A Path Planning Method for Human Tracking 

Agents using Variable-term Prediction based on 

dynamic k Nearest Neighbor Algorithm 

Noriko Takemura, Yutaka Nakamura, and Hiroshi Ishiguro 

Graduate School of Engineering Science, Osaka University, Japan 

• A path planning method for human 

tracking by multiple agents. 

• Agents’ paths are planned based on 

similarity between the humans’ positions 

and the agents’ FOV. 

• The horizon length of the planned path is 

determined according to the accuracy of 

predicted human positions. 

• Simulation experiments showed that 

agents can keep tracking human 

movements recorded in a real 

environment. 

Recorded human movements 

used for simulation experiment 

(Kyoto station) 

14:30–14:45 WeCT1.3 

Sound Source Localization Based on 

Time Difference Feature and Space Grid Matching 

Xiaofei Li, Hong Liu and Xuesong Yang 

Shenzhen Graduate School, Peking University, CHINA 

• A new spectral weighting GCC-PHAT 

method estimates robust time difference 

of arrival in real environments. 

• Space is divided into grids represented 

by Gaussian Mixture Model based on 

time difference feature. 

• Finding the maximum likelihood grid with 

the current time difference feature in 

localization step. 

• Decision tree reduces the number of 

matches greatly. 

The partition of horizontal space 

and the process of localization 

14:15–14:30 WeCT1.2 

Using Human Motion Estimation for 

Human-Robot Cooperative Manipulation 

Anand Thobbi, Ye Gu and Weihua Sheng 


Oklahoma State University, USA. 

• We propose a novel approach that allows 

a humanoid robot to determine its 

leader/follower role in a collaborative 

manipulation task 

• Behavior of the robot is governed by a 

weighted sum of proactive and reactive 

controllers 

• Reactive controller learnt using 

reinforcement learning 

• Design of the proactive controller is based 

on human motion prediction 

• Proposed framework is demonstrated on a 

joint table lifting task 


–131– 

Experimental Setup and Results 

14:45–14:50 WeCT1.4 

Adapting Robot Team Behavior from Interaction 

with a Group of People 

P. Urcola and L. Montano 

Instituto de Investigación en Ingeniería de Aragón 

Universidad de Zaragoza, Spain 

• A team of robots cooperate to detect and track people using laser range 

finder sensors 

• Group motion and people entering and leaving the group are detected 

• The behavior of the team is adapted to the people and to the 

environment 

• The system is applied in a group guiding mission scenario 

• Simulation tests and experiments in a real environment are presented 

Group guiding mission experiment. 

The perception of the team is on top right corner


Human-Robot Interaction and Cooperation 

Chair Weihua Sheng, Oklahoma State Univ. 

Co-Chair Rachid Alami, CNRS 

14:50–14:55 WeCT1.5 

Towards a Platform-Independent Cooperative 

HRI System: II. Perception, Execution and 

Imitation of Goal Directed Actions 

S. Lallée, U. Pattacini, JD. Boucher, S. Lemaignan, A. Lenz, C. 

Melhuish, L. Natale, S. Skachek, K. Hamann, J. Steinwender, EA 

Sisbot, G. Metta, R. Alami, M. Warnier, J. Guitton, F. Warneken, 

PF. Dominey 

• Action perceptual & motor 

representations linked within 

the same datastructure 

• Actions can be recognized, 

performed and used in 

Shared Plan in cooperation 

with a human 

• Robotic platform 

Independence is guaranteed 

through the use of the 

EgoSphere abstraction layer 

15:00–15:15 WeCT1.7 

Improvement of Speaker Localization 

by Considering Multipath Interference of Sound Wave 

for Binaural Robot Audition 

Ui-Hyun Kim, Takeshi Mizumoto, 

Tetsuya Ogata, and Hiroshi G. Okuno 

Department of Intelligence Science and Technology, 

Graduate School of Informatics, Kyoto University, Japan 

• This paper presents an improved speaker 

localization based on the GCC-PHAT 

method for binaural robot audition. 

• We proposed a new time delay factor for 

the GCC-PHAT method to compensate 

multipath interference due to diffraction of 

the sound wave after assuming that the 

robot head is spherical. 

• Experiments conducted in the SIG-2 

humanoid robot show that the proposed 

method reduces localization errors by 17.8 

degrees on average and by over 35 

degrees in side directions more than the 

conventional DOA estimation. 

RMSE (degree) 

Multipath interference 

with spherical-head assumption 

40 

30 

20 

10 

0 

Conventional DOA estimation 

Proposed DOA estimation #1 



-50 0 

Azimuth (degree) 

50 

RMSE of experimental results 

in speaker localization 

14:55–15:00 WeCT1.6 

Listening for people: Exploiting the spectral 

structure of speech to robustly perceive 

the presence of people 

Barbara Hilsenbeck 

Faculty of Mechanical and Mechatronic Engineering 

Karlsruhe University of Applied Sciences, Karlsruhe, Germany 

Nathan Kirchner 

Centre for Autonomous Systems, University of Technology, Sydney, Australia 

• As the desire to see robots 

ubiquitous in society grows, so 

does the need for providing the 

robots with the means of 

building awareness of any 

humans with which it may be 

sharing the environment 

• This paper presents a realworld 

suitable system which 

enables robots to robustly 

perceive the presence of 

people acoustically 

15:15–15:30 WeCT1.8 

Robot Audition & Beat Identification in Noisy 

Environments 

David K. Grunberg, Daniel M. Lofaro, Paul Oh, and Y. E. Kim 

College of Engineering, Drexel University, USA 

• Vision: use beat tracking algorithms for 

enabling the Hubo robot to participate in 

musical ensembles. 

• Musical participation requires the ability to 

determine auditory beat locations despite 

acoustic noise. 

• We explore the use of adaptive filtering to 

eliminate acoustic noise for greater 

musical accuracy. 


–132– 

The Hubo robot


Pose Estimation & Visual Tracking 

Chair Evangelos Papadopoulos, National Tech. Univ. of Athens 

Co-Chair Wim Meeussen, Willow Garage inc. 

14:00–14:15 WeCT2.1 

Determination of Rigid-Body Pose from 

Imprecise Point Position Measurements 

Anastasia Tegopoulou and Evangelos Papadopoulos 

Department of Mechanical Engineering, 

National Technical University of Athens, Athens, Greece 

• Determine rigid-body position and 

orientation from the position of a number 

of its points 

• The novel proposed method (NM) uses 

normal vectors, by-passing frame 

definitions 

• The NM takes advantage of point 

interconnections and is computationally 

efficient 

• The NM yields reliable results both with 

absolute and relative position sensors 

and both with low and high noise levels 

Two consecutive time instants for 

three point position measurements 

during planar motion 

14:30–14:45 WeCT2.3 

Simultaneous Localization and Capture 

with Velocity Information 

Qilong Yuan, I-Ming Chen 

School of Mechanical and Aerospace Engineering, 

Nanyang Technological University, Singapore 

• This paper introduces a method to 

monitor and capture the velocity, 

location, and dynamic behavior of a 

human with a self motion capture suit. 

• Velocity from lower limb kinematics 

and the integration of accelerations 

are fused through Kalman Filters to 

achieve smooth, accurate and drift 

free estimation results. 

• Capture of spatial motion for dynamic 

behaviors like running and jumping is 

realized. 

Spatial Motion for Jumping 

14:15–14:30 WeCT2.2 

Pose Estimation from a Single Image using Tensor 

Decompositions and an Algebra of Circulunts 

Randy C. Hoover 

Dept. of Electrical and Computer Engineering, SDSM&T, USA 

Karen S. Braman 

Dept. of Mathematics and Computer Science, SDSM&T, USA 

Ning Hao 

Dept. of Mathematics, Tufts, USA 

• New tensor SVD described using an 

algebra of circulunts 

• Experimental results applied to 

image analysis and interpretation 

• Able to reconstruct image set 

information using a much lower 

subspace dimension 

• Accurate pose estimation for one 

dimensionally correlated sequence 

using a single t-eigenimage 

• Analyze image sequences containing 

multiple degrees of correlations 

(multi-modal analysis) 


–133– 

� 

14:45–14:50 WeCT2.4 

Outlet detection and pose estimation for robot 

continuous operation 

Victor Eruhimov 

Itseez, Russia 

Wim Meeussen 

Willow Garage, USA 

• Outlet detection algorithm uses a single 

monocular camera 

• It locates 6 holes of a 2x1 outlet structure 

• A new pose estimation algorithm finds 

outlet holes 3D coordinates with submillimeter 

accuracy 

• It uses an image from a monocular 

camera and a wall normal orientation 

obtained from a stereo camera 

• The algorithm shows no false positives on 

a test set of 500+ outlet images 

• The plugin function enables PR2 

continuous operation that lasted for 13 

days


Pose Estimation & Visual Tracking 

Chair Evangelos Papadopoulos, National Tech. Univ. of Athens 

Co-Chair Wim Meeussen, Willow Garage inc. 

14:50–14:55 WeCT2.5 

Robust Tracking of Human Hand Postures 

for Robot Teaching 

Jonathan Maycock, Jan Steffan, 

Robert Haschke, and Helge Ritter 

Neuroinformatics, Bielefeld University, Germany 

• Three methods for real-time tracking 

of hands are presented 

• A dataglove approach and two 

VICON approaches (one direct 

calculation and inverse kinematics 

approach) 

• Qualitative and quantitative are 

presented 

• Inverse kinematics approach found 

to have performed best for both 

power and precision grasps 

15:00–15:15 WeCT2.7 

Visual Tracking of Robots in Uncalibrated 

Environments 

Hesheng Wang and Weidong Chen 

Department of Automation, Shanghai Jiao Tong University, China 

Zhongli Wang 

Department of Automation, Beijing Jiao Tong University, China 

• Proposed a new adaptive controller for 

trajectory tracking of a number of feature 

points on a robot manipulator 

• Developed an adaptive algorithm to 

estimate the unknown parameters on-line 

• Proved asymptotic convergence of 

tracking errors by Lyapunov method and 

verified performance by experiments 

Visual tracking system 

14:55–15:00 WeCT2.6 

Visual Tracking Using the 

Sum of Conditional Variance 

Rogerio Richa, Raphael Sznitman, Russell Taylor, Gregory Hager 

Laboratory for Computational Science and Robotics (LCSR) 

Johns Hopkins University, Baltimore MD, USA 

• The SCV is a similarity metric used 

in the medical imaging domain for 

multi-modal image registration 

• Applied to visual tracking, the SCV 

is invariant to non-linear 

illumination variations and is 

computationally inexpensive 

• Compared to other similarity 

metrics, it requires significantly less 

iterations for convergence and has 

a larger convergence radius 

15:15–15:30 WeCT2.8 

Hybrid Discriminative Visual Object Tracking 

with Confidence Fusion for Robotics 

Applications 

Ren C. Luo, Ching-Chung Kao, and Yen-Chang Wu 

Electrical Engineering Department, National Taiwan University, Taiwan 

• In this paper, we propose a hybrid visual tracking algorithm that 

combines two discriminative trackers 

• The two trackers collectively determine the new object location via a 

process called confidence fusion 

• One component tracker is aimed to discriminate the target from the 

background in pixel level while the other is aimed to do so in patch level 

• The resultant hybrid tracker is hopefully more powerful since the two 

component trackers can complement the ability of discrimination 


–134–


Robot Safety 

Chair Richard Voyles, Univ. of Denver 

Co-Chair Irene Sardellitti, Italian Inst. of Tech. 

14:00–14:15 WeCT3.1 

Towards safe human-robot interaction in robotic 

cells: an approach based on visual tracking and 

intention estimation 

Luca Bascetta, Gianni Ferretti and Paolo Rocco 

Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italia 

Håkan Ardö 

Department of Mathematics, Lund University, Lund 

Herman Bruyninckx, Eric Demeester and Enrico Di Lello 

Department of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium 

• Human-robot interaction requires the 

elimination of the safety fences 

• A new functionality of the safety 

controller – to detect and track the 

humans in the cell and to estimate their 

intentions – is required 

• The overall goal is to predict in the least 

possible time which area in the vicinity 

of the robot the human is heading to 

14:30–14:45 WeCT3.3 

Relaxing the Inevitable Collision State Concept to Address 

Provably Safe Mobile Robot Navigation with 

Limited Field-of-View in Unknown Dynamic Environments 

Sara Bouraine 1 , Thierry Fraichard 2 and Salhi Hassen 3 

1 CDTA , Algeria, 2 INRIA, France, 3 Blida University, Algeria 

• How to guarantee motion safety in partially known dynamic environments? 

• Absolute motion safety impossible to guarantee 

• Settle for weaker motion safety: passive motion safety (PMS) = if a 

collision must take place, robot is at rest 

• PMS obtained via Braking ICS, a relaxation of regular ICS (Inevitable 

Collision State) 

• Braking ICS checking algorithm designed and used to obtain provably 

passively safe navigation 

14:15–14:30 WeCT3.2 

Guaranteed Safe Online 

Learning of a Bounded System 

Jeremy Gillula 

Computer Science Dept., Stanford, USA 

Claire Tomlin 

EECS Dept., UC Berkeley, USA 

• Many machine learning algorithms are 

limited to situations where safety is not 

critical, since they lack guarantees about 

their stability and robustness 

• This can be solved by augmenting 

learning algorithms with reachability 

analysis, a general technique for making 

safety guarantees 

• We apply these ideas to a scenario in 

which an aerial robot is attempting to learn 

the dynamics of a ground vehicle 

• The resulting simulations show that by 

combining these two paradigms, one can 

create robotic systems that feature both 

high performance and guaranteed safety 


–135– 

Simulated example application: 

tracking a ground target using an 

aerial vehicle. 

14:45–14:50 WeCT3.4 

Capacitive Skin Sensors 

for Robot Impact Monitoring 

Samson Phan, Zhan Fan Quek, Preyas Shah, Zubair Ahmed, and 

Mark Cutkosky 

Mechanical Engineering, Stanford University, USA 

Dongjun Shin and Oussama Khatib 

Computer Science, Stanford University, USA 

• Capacitive sensors are embedded in an 

energy-absorbing artificial skin 

• Sensor scanning provides the history of 

a collision, including force magnitudes 

and contact locations 

• Impact testing demonstrates ability to 

discern effects of velocity, collision 

angle, contact stiffness, etc. 

Test setup and sequence of sensor 

readings for a sliding contact


Robot Safety 

Chair Richard Voyles, Univ. of Denver 

Co-Chair Irene Sardellitti, Italian Inst. of Tech. 

14:50–14:55 WeCT3.5 

Variable Radius Pulley Design Methodology for 

Pneumatic Artificial Muscle-based Antagonistic 

Actuation Systems 

Dongjun Shin1 , Xiyang Yeh2 , Oussama Khatib1 1 

Artificial Intelligence Laboratory, Stanford University, USA 

2 

Mechanical Engineering , Stanford University, USA 

• Analysis of the limitations of 

conventional circular pulleys 

• A new design methodology for 

variable radius pulleys 

• Experimental comparisons to 

show performance improvement in 

position control in the enlarged 

workspace. 

Variable Radius Pulleys in Pneumatic 

Muscle-driven 1-DOF Testbed 

15:00–15:15 WeCT3.7 

Online Data-Driven Fault Detection 

for Robotic Systems 

Raphael Golombek and Sebastian Wrede 

CoR-Lab, Bielefeld University, Germany 

Marc Hanheide 

School of Computer Science, University of Birmingham, UK 

Martin Heckmann 

Honda Research Institute Europe, Offenbach, Germany 

• Fault detection applicable for online as 

well as a-posteriori analysis 

• Exploits inter-component 

communication in order to model 

normal system behaviour 

• Directly applicable to systems built upon a 

data-driven or event-based robotics 

middle-ware concept 

• Flexible design allows to adapt to 

systems which do not fit the 

aforementioned architectural concepts 

Conceptual Overview of the 

Data-Driven Fault Detection 

Approach 

14:55–15:00 WeCT3.6 

Fast Computation of Wheel-Soil Interactions for 

Safe and Efficient Operation of Mobile Robots 

Zhenzhong Jia, William Smith and Huei Peng 

Ground Robotics Reliability Center (GRRC), University of Michigan, USA 

Department of Mechanical Engineering, University of Michigan, USA 

• Fast analytical models of wheel-soil 

interactions are important for mobile 

robots in off-road environments. 

• An accurate, fully-functional, closed-form 

wheel-terrain interaction model was 

developed by quadratic approximation of 

the stress distributions. 

• A non-iterative method was derived to 

estimate the entry angle when given the 

normal wheel load. 

• Mobility prediction and real-time vehicle 

control become possible by coupling the 

wheel-soil interaction model and the entry 

angle estimator. 


–136– 

Determine forces/torques by 

integrating the stress distributions 

15:15–15:30 WeCT3.8 

Containment indicator function construction via 

numerical conformal mapping 

Shuo Han and Richard M. Murray 

California Institute of Technology, Pasadena, USA 

• Numerical conformal mapping is used to 

obtain a smooth containment indicator 

function for optimal motion planning 

• The procedure can be approximated by 

solving a convex optimization problem 

• Several different 2D shapes have been 

tested 

• Can also be extended to modeling 

obstacles without much difficulty

Session WeCT4 Continental Ballroom 4 Wednesday, September 28, 2011, 14:00–15:30 

Symposium: Haptic Feedback and System Evaluation 

Chair Marcia O'Malley, Rice Univ. 

Co-Chair Yasuyoshi Yokokohji, Kobe Univ. 

14:00–14:15 WeCT4.1 

The sigma.7 haptic interface for MiroSurge: 

A new bi-manual surgical console 

Andreas Tobergte¹, Patrick Helmer², Ulrich Hagn¹, 

Patrice Rouiller², Sophie Thielmann¹, Sebastien Grange², 

Alin Albu-Schäffer¹, Francois Conti², and Gerd Hirzinger¹ 

German Aerospace Center (DLR) ¹ 

Force Dimension² 

• Design of the haptic devices and the surgical console 

• Kinematics and dynamics 

• Control with force/torque sensor 

• Ergonomics of the console 

14:30–14:45 WeCT4.3 

Measuring an Operator’s Maneuverability 

Performance in the Haptic Teleoperation of 

Multiple Robots 

Hyoung Il Son 1 , Lewis L. Chuang 1 , Antonio Franchi 1 , Junsuk Kim 2 , 

Dongjun Lee 3 , Seong-Whan Lee 2 , Heinrich H. Bülthoff 1,2 , 

and Paolo Robuffo Giordano 1 

1 Max Planck Institute for Biological Cybernetics, Germany; 

2 DBCE, Korea University, Korea; 3 DMAE, Seoul National University, Korea 

• Maneuverability measure to assess the ease 

of the operator in maneuvering the remote 

multi-robot slave 

• Three haptic cues to perceive 

the state of the remote UAVs and their 

surrounding environments: 

1. Velocity cue 

2. Force cue 

3. Velocity+Force cue 

Haptic Device 

• Evaluation by using H 2 norm and 

bandwidth of the maneuverability measure 

• Results indicate that less control effort 

is required in achieving the same level of 

tracking accuracy with Force cue 

Velocity 

Command 

Tele-Operator’s 

Maneuverability 

Haptic 

Feedback 

JND 

Multiple UAVs 

14:15–14:30 WeCT4.2 

Haptic coupling with augmented feedback between 

two KUKA LWR’s and the PR2 robot arms 

Koen Buys, Steven Bellens, Wilm Decre, 

Ruben Smits, Enea Scioni, Tinne De Laet, 

Joris De Schutter, and Herman Bruyninckx 

Department Mechanical Eng., KU Leuven, Belgium 

• Coupling between Orocos and ROS 

robotic frameworks 

• Haptic feedback setup with non-uniform 

robot coupling 

• Coupling of head movement 

• Augmented visual feedback in VR goggles 

• Quantitative force measurements in 

experiments 

14:45–14:50 WeCT4.4 

Bilateral Physical Interaction with a 

Robot Manipulator through a 

Weighted Combination of Flow Fields 

Antonio Pistillo, Sylvain Calinon and Darwin G. Caldwell 

Department of Advanced Robotics, Istituto Italiano di Tecnologia, Italy 

• Tasks demonstrated by kinesthetic 

teaching technique and encoded 

with a combination of local flow 

fields. 

• The weighting mechanism modifies 

standard GMM weights to be 

independent from each other. 

• Safe Physical Interaction between 

the user and the robot is achieved. 

• The robot is used as a bi-directional 

tangible interface in order to 

influence its actions. 

• The user can start, stop, pause, 

resume and select desired robot 

movements by physically guiding it 

during task executions. 


–137–


Symposium: Haptic Feedback and System Evaluation 

Chair Marcia O'Malley, Rice Univ. 

Co-Chair Yasuyoshi Yokokohji, Kobe Univ. 

14:50–14:55 WeCT4.5 

Motion Control of a Semi-Mobile Haptic Interface 

for Extended Range Telepresence 

Antonia Pérez Arias and Uwe D. Hanebeck 

Intelligent Sensor-Actuator-Systems Laboratory (ISAS), 

Karlsruhe Institute of Technology (KIT), Germany 

• Prepositioning algorithm for haptic 

exploration of spatially unrestricted 

target environments 

• Key idea: Consideration of user’s 

direction of motion 

• Benefits: 

• Optimal utilization of available 

space in user environment 

• Robustness against noisy 

measurements of user position 

• Experimental validation under 

real-world conditions 

Semi-mobile haptic interface and 

operator in user environment. 

15:00–15:15 WeCT4.7 

Assistance or Challenge? 

Filling a Gap in User-Cooperative Control 

1Georg Rauter, 1Roland Sigrist, 1Laura Marchal-Crespo, 

1, 2Heike Vallery, 1Robert Riener, and 1Peter Wolf 

1SMS-Lab, Institute of Robotics and Intelligent Systems (IRIS), ETH-Zurich & 

Medical Faculty,University of Zurich, Switzerland 

2Biomedical Engineering, Khalifa University of Science, Technology & 

Research (KUSTAR), Abu Dhabi, UAE 

• Haptic feedback might be effective if the right amount of guidance or 

challenge is provided 

• We developed a user-cooperative controller that can be continuously 

modified from supportive to challenging mode 

• In a pilot study, the controller could proof its applicability for motor learning 

studies 

Rowing simulator: Haptic device consists of a tendon-based parallel robot 

14:55–15:00 WeCT4.6 

An Objective Index that Substitutes for Subjective 

Quality of Vibrotactile Material-Like Textures 

Shogo Okamoto and Yoji Yamada, 

Dept. of Mechanical Science and Engineering, Nagoya University, Japan 

• For the delivery of tactile contents through the Internet, the lossy data 

compression of vibrotactile textures are expected. 

• An objective index that evaluates the quality deterioration of the lossycompressed 

textures is necessary for an alternative for psychological 

experiments. 

• This study developed such 

an index based on the 

amplitude spectrum of 

vibrotactile stimuli. 

• The index was applied to 

wood and sandpaper 

textures and described the 

subjective quality indices by 

approximately 80%. 


–138– 

Developed objective index 

Quality indices of compressed vibrotactile 

material-like textures 

Subjective indices acquired by 

psychological experiments 

15:15–15:30 WeCT4.8 

Design and Characterization of the ReHapticKnob, a 

Robot for Assessment and Therapy of Hand Function 

Jean-Claude Metzger, Olivier Lambercy, 

Dominique Chapuis, Roger Gassert 

Rehabilitation Engineering Laboratory, ETH Zurich, Switzerland 

• End-effector rehabilitation robot with 

unique sensing and actuation capabilities 

• 2 active DOF for training of hand 

opening/closing (88N) and forearm 

rotation (0.98Nm) tasks 

• Developed for precise and objective 

assessments of hand function and for high 

fidelity dynamic interaction 

• Preliminary grasping results with healthy 

subjects


Symposium: Symbolic Approaches to Motion Planning and Control 

Chair Hadas Kress-Gazit, Cornell Univ. 

Co-Chair Calin Belta, Boston Univ. 

14:00–14:15 WeCT5.1 

LTL-Based Decentralized Supervisory Control of 

Multi-Robot Tasks Modelled as Petri Nets 

Bruno Lacerda and Pedro U. Lima 

Institute for Systems and Robotics, Instituto Superior Técnico, Portugal 

• Methodology to enforce coordination rules s 

for multi-robot systems 

• Each robot is modelled as a Petri net and 

coordination rules are written in LTL 

• Decentralized approach, uses the LTL 

specifications to define the communication n 

requirements between the robots 

• Greatly improves scalability when 

compared to centralized approach 

The Th inputs i t of fth the methodology: th d l 

A Petri net model of a robot and 

an LTL formula for the robot to fulfill 

14:30–14:45 WeCT5.3 

Foundations of Formal Language for Humans 

and Artificial Systems Based on Intrinsic 

Structures in Spatial Behaviors 

Zhaodan Kong and Bernie Mettler 

Department of Aerospace Engineering and Mechanics 

University of Minnesota, USA 

• Interactions between vehicle 

dynamics and environment result in 

patterns in spatial behavior. 

• Preliminary results studying these 

patterns show that behavior exhibits 

well defined hierarchic structure. 

• Results can be used to create a 

formal language, which can be used 

to mitigate complexity in planning and 

representation. 

• Gained insight will also help 

understand spatial intelligence and 

help design adaptive guidance 

algorithms. 

Optimal Trajectories of Raven 

UAV in Downtown San Diego 

14:15–14:30 WeCT5.2 

Optimal Multi-Robot Path Planning with Temporal 

Logic Constraints 

Alphan Ulusoy 

Systems Eng., Boston University, USA 

Stephen L. Smith 

Electrical and Computer Eng., University of Waterloo, Canada 

Xu Chu Ding and Calin Belta 

Systems Eng., Boston University, USA 

Daniela Rus 

EECS, Massachusetts Institute of Technology, USA 

• A method for planning optimal paths for a 

group of robots that satisfy a common 

mission. 

• The mission is given as a Linear Temporal 

Logic formula. 

• In addition, an optimizing proposition must 

repeatedly be satisfied. 

• The goal is to minimize the maximum time 

between the satisfying instances of the 

optimizing proposition. 

• Method utilizes a timed automaton 

representation to capture asynchronous 

robot motions. 


–139– 

Simulated team trajectories 

14:45–14:50 WeCT5.4 

Minimalist Multiple Target Tracking Using 

Directional Sensor Beams 

Leonardo Bobadilla, Oscar Sanchez, Justin Czarnowski 


Department of Computer Science, University of Illinois, USA 

• Multiple-agent tracking is a fundamental 

problem of sensor networks with broad 

applications. 

• We propose simple and efficient 

algorithms for reconstructing paths of 

agents using weak detection sensors. 

• Designed and implemented a low-cost, 

low energy consumption hardware 

architecture to demonstrate the practicality 

proposed approach. 

a) Two randomly moving bodies 

b) Ground truth paths 

c) Reconstructed paths 

based on our algorithm


Symposium: Symbolic Approaches to Motion Planning and Control 

Chair Hadas Kress-Gazit, Cornell Univ. 

Co-Chair Calin Belta, Boston Univ. 

14:50–14:55 WeCT5.5 

Temporal Logic Control in Dynamic Environments 

with Probabilistic Satisfaction Guarantees 

Ana Medina Ayala, Sean Andersson and Calin Belta 

Mechanical Engineering, Boston University, United States 

• A robot with noisy sensors and 

actuators and moving in a dynamic 

environment is considered. 

• The task is given as a specification in 

Probabilistic Computation Tree Logic 

(PCTL). 

• Algorithms for synthesizing a control 

policy to maximize the probability of 

satisfaction are derived. 

• Different levels of knowledge of 

environment dynamics are 

considered. 

15:00–15:15 WeCT5.7 

High-Level Control of Modular Robots 

Sebastian Castro, Sarah Koehler and Hadas Kress-Gazit 

Sibley School of Mechanical Engineering, Cornell University, USA 

• Generation of provably correct 

controllers from structured English 

specifications with reconfigurable 

modular robots. 

• Utilizes the LTLMoP framework with the 

addition of reconfiguration via descriptors 

called traits. 

•A configuration-gait-trait library 

automatically selects robot morphologies 

given a set of active traits. 

• Demonstrated through the CKBot 

platform and a 3D simulator built on a 

physics engine. 

Sample Workspace and Task 

Execution with the CKBot platform. 

14:55–15:00 WeCT5.6 

Computing Unions of Inevitable Collision States 

and Increasing Safety to Unexpected Obstacles 

Daniel Althoff 1 , Christoph N. Brand 1 , 

Dirk Wollherr 1,2 and Martin Buss 1 

Institute of Automatic Control Engineering, TUM, Germany 1 

Institute for Advanced Study, TUM, Germany 2 

• Problem of computing unions of Inevitable 

Collision State (ICS) sets is addressed 

• Sequential computation of ICS is shown 

• Two novel ICS-Checker algorithms are 

presented allowing efficient computation 

• Concept of robot Maneuverability is introduced, 

reducing collision probability to unexpected 

obstacles 

• Simulation results validate these concepts 


–140– 

Top: Union of singel ICS sets 

Bottom: ICS set of union of 

obstacles 

15:15–15:30 WeCT5.8 

Synthesis of Feedback Controllers for Multiple 

Aerial Robots with Geometric Constraints 

Nora Ayanian, Vinutha Kallem and Vijay Kumar 

Department of Mechanical Engineering and Applied Mechanics 


• Algorithm for multi-robot navigation 

with convergence and safety 

guarantees 

• Kinematic and dynamic agents 

• Maintain communication and avoid 

collisions 

• Simultaneously decompose joint 

configuration space into polytopes 

and plan a discrete path using A* 

• Sequentially deploy navigationfunction 

based local controllers on 

polytopes to reach goal 

• Implementations on teams of 

ground robots and quadrotors 

A quadrotor (red) and two ground robots 

(blue, green) deploy at left to cover an 

intersection in an urban environment 

while maintaining communication.


Symposium: Marine Robotics: Platforms and Applications 

Chair Ryan Smith, Queensland Univ. of Tech. 

Co-Chair Gianluca Antonelli, Univ. degli Studi di Cassino 

14:00–14:15 WeCT6.1* 

Semi-Plenary Invited Talk: Wiring an Interactive Ocean 

John Delaney, University of Washington 

14:30–14:45 WeCT6.3 

Towards Mixed-initiative, Multi-robot Field 

Experiments: Design, Deployment, and Lessons 

Learned 

Jnaneshwar Das, Gaurav S. Sukhatme 

Department of Computer Science, University of Southern California, USA 

Thom Maughan, Mike McCann, Mike Godin, Tom O’Reilly, 

Monique Messié, Fred Bahr, Kevin Gomes, Frédéric Py, 

James G. Bellingham, Kanna Rajan 

Monterey Bay Aquarium Research Institute, USA 

• Description of a month-long multi-institute 

field experiment with multiple 

heterogeneous robotic platforms. 

• Design, development, and use of an 

Oceanographic Decision Support System 

(ODSS) during the experiment. 

• Lessons learned and discussion of future 

roadmap. 

14:15–14:30 WeCT6.2 

Semi-Plenary Invited Talk: Wiring an Interactive Ocean 

John Delaney, University of Washington 

14:45–14:50 WeCT6.4 

Current-Sensitive Path Planning for an 

Underactuated Free-floating Ocean Sensorweb 

Kristen P. Dahl 


David R. Thompson, David McLaren, Yi Chao, and Steve Chien 

Jet Propulsion Laboratory, California Institute of Technology, USA 

• This work investigates multiagent path 

planning in strong, dynamic currents using 

highly underactuated vehicles. 

• The Argo global network consists of over 

3000 ocean-observing floats able to 

control only depth. 

• Accurate current forecasts present the 

possibility of intentionally controlling floats’ 

motion. 

• Simulations tested this method’s potential 

to improve coverage or direct floats to a 

desired location. 


–141– 

A simulation in which five floats 

use current-driven planning to 

improve coverage. 

(yellow: non-adaptive; 

blue: adaptive)


Symposium: Marine Robotics: Platforms and Applications 

Chair Ryan Smith, Queensland Univ. of Tech. 

Co-Chair Gianluca Antonelli, Univ. degli Studi di Cassino 

14:50–14:55 WeCT6.5 

Toward Risk Aware Mission Planning for 

Autonomous Underwater Vehicles 

Arvind A. Pereira, Jonathan Binney, and Gaurav S. Sukhatme 


Burton H. Jones and Matthew Ragan 

Department of Biological Sciences, University of Southern California, USA 

• Creation of probabilistic occupancy 

maps from AIS data 

• Plan minimum risk paths for AUVs 

that avoid risky regions 

• Paths planned enforce AUV 

resurfacing within a maximum 

distance from previous waypoint 

• Significant surface collision risk 

reduction compared to shortest path 

and other methods 

15:00–15:15 WeCT6.7 

Obstacle Detection from Overhead Imagery 

using Self-Supervised Learning for 

Autonomous Surface Vehicles 

Hordur K. Heidarsson 

Department of Electrical Engineering, University of Southern California, USA 

Gaurav S. Sukhatme 


• Technique for generating an 

obstacle map from sonar data 

and overhead imagery 

• Pipeline consisting of 

• Feature extraction 

• AdaBoost classifier 

• MRF smoothing 

• Combining images from 

different times 

• Resulting map has 3 classes 

• Obstacles 

• Transient/suspect obstacles 

• Free space 

14:55–15:00 WeCT6.6 

AUV Docking on a Moving Submarine Using a 

K-R Navigation Function 

Sujit. P.B and Joao Sousa 

Department of Electrical and Computer Eng., University of Porto, Portugal 

Anthony, J Healey 

Naval Postgraduate School, Monterey, CA, USA 

• The objective is to design a controller for 

the AUV to dock onto a moving submarine 

• Developed a Navigation function approach 

that integrates path planning with collision 

avoidance 

• Modeled no-fly zones around stern to 

protect propulsor and mitigate against 

disturbances 

• Other no-fly zones included around the 

dock to control approach headings 

• Gain must be selected appropriately to 

avoid local minima 

• Simulation results show the desirability of 

the approach from different directions 


–142– 

AUV docking on a moving 

submarine from stern direction 

15:15–15:30 WeCT6.8 

Observability metric for the relative localization of AUVs 

based on range and depth measurements: 

theory and experiments 

Filippo Arrichiello and Gianluca Antonelli 

DAEIMI, University of Cassino, Italy 

Antonio Pedro Aguiar and Antonio Pascoal 

IST, Technical University Lisbon, Portugal 

• The work addresses the problem of 

observability of the relative motion of two 

AUVs equipped with, velocity, range and 

depth sensors 

• We exploited nonlinear observability 

concepts to analyze some types of 

relative AUV motions 

• We derive a specific observability index 

(metric) and study its dependence on the 

types of motion commanded to the 

vehicles. 

• The results derived are validated 

experimentally in a equivalent, single 

beacon navigation scenario 

Autonomous surface vessel used 

for the experimental validation


Humanoid Control 

Chair Eiichi Yoshida, National Inst. of AIST 

Co-Chair Alessandro De Luca, Univ. di Roma "La Sapienza" 

14:00–14:15 WeCT7.1 

Logic Programming with Simulation-based Temporal 

Projection for Everyday Robot Object Manipulation 

Lars Kunze, Mihai E. Dolha, Michael Beetz 

Intelligent Autonomous Systems 

Technische Universitaet Muenchen 

Germany 

• Robots must decide on appropriate action ction 

parameterizations based on the expected cted 

consequences of their actions 

• Logic programming with simulation-based sed 

temporal projection can predict the 

consequences of manipulation actions s 

qualitatively 

• Hence, robots can employ these methods ods 

to infer action parameterizations that lead ead 

to desired outcomes 

Logical proof-tree of simulation- 

based temporal projections 

14:30–14:45 WeCT7.3 

Switching Multiple LQG Controllers 

Based on Bellman's Optimality Principle 

Norikazu Sugimoto1 and Jun Morimoto2 1National Institute of Communication Telecommunication, Kyoto, Japan 

2ATR Computational Neuroscience Labs, Kyoto, Japan 

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Deviation from Bellman’s optimality principle 

14:15–14:30 WeCT7.2 

Experimental Evaluation of a Trajectory/Force 

Tracking Controller for a Humanoid Robot 

Cleaning a Vertical Surface 

Fuyuki Sato, Tatsuya Nishii, Jun Takahashi, 

Yuki Yoshida, Masaru Mitsuhashi and Dragomir Nenchev 

Mechanical Systems Engineering, Tokyo City University, Japan 

• The force tracking controller is based on 

CoM ground projection and ZMP position 

feedback 

• The hand trajectory is tracked via a PD 

controller 

• The trajectory/force tracking controller is 

implemented under a mixed 

position/torque control mode 

• The performance of the controller is 

experimentally evaluated with a miniature 

humanoid robot HOAP-2 cleaning a 

whiteboard under a prerecorded 

motion/force pattern 


–143– 

A HOAP-2 humanoid robot 

cleaning a whiteboard 

14:45–14:50 WeCT7.4 

Adaptive Predictive Gaze Control 

of a Redundant Humanoid Robot Head 

Giulio Milighetti and Luca Vallone 

Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, 

Germany 

Alessandro De Luca 

Dipartimento di Informatica e Sistemistica, Univ. di Roma “La Sapienza”, Italy 

• General concept for gaze control of 

redundant humanoid robot heads 

• Prediction of target next state by adaptive 

Kalman filter 

• Combination of proportional feedback and 

feedforward term for trajectory tracking 

control 

• Gain adaptation for improvement of 

system dynamics 

• Exploitation of inverse kinematics by 

means of weighted pseudoinverse 

• Addition of self-motions for human-like 

head postures 

ARMAR-III Head tracking a cup


Humanoid Control 

Chair Eiichi Yoshida, National Inst. of AIST 

Co-Chair Alessandro De Luca, Univ. di Roma "La Sapienza" 

14:50–14:55 WeCT7.5 

Dynamic Whole-Body Mobile Manipulation with 

a Torque Controlled Humanoid Robot via 

Impedance Control Laws 

Alexander Dietrich, Thomas Wimböck, and Alin Albu-Schäffer 


• Cartesian impedance is utilized for task 

execution 

• Incorporation of reactive subtasks like 

collision avoidance, singularity avoidance, 

mechanical end stops, null space damping 

• Experimental validation of the proposed 

whole-body control 

• A video demonstrates the performance of 

our approach 

Torque Controlled Humanoid 

Rollin’ Justin 

15:00–15:15 WeCT7.7 

A Computational Approach for Push Recovery in 

case of Multiple Non coplanar Contacts 

Darine Mansour and Alain Micaelli 

CEA, France 

Pierre Lemerle 

INRS, France 

• A push recovery approach based on a 

humanoid simple model is proposed 

•A dynamic stability indicator 

predicting whether a perturbed system 

can be stabilized is generated 

• An algorithm stabilizing the system 

under fixed contacts when feasible, 

is developed 

• An algorithm stabilizing the system 

through an optimal contact change 

when needed, is developed 

[1.] Bretl and S. Lall, "Testing Static Equilibrium for Legged 

Robots", in IEEE Transactions on Robotics, Aug 2008 

Simplified Model of a humanoid 

The model consists of a punctual 

mass at the COM and (n) non 

coplanar contact surfaces. It is an 

extension of the model in [1.] 

14:55–15:00 WeCT7.6 

Gait Pattern Generation and Stabilization for 

Humanoid Robot Based on Coupled Oscillators 

Inyong Ha, Yusuke Tamura, and Hajime Asama 

Department of Precision Engineering, The University of Tokyo, Japan 

• Design of gait pattern generator 

based on coupled oscillator model 

divided into movement oscillator 

and balance oscillator 

• Walking stabilizer by feedback 

controller using oscillator 

parameters and sensor data 

• Tested on open humanoid robot 

platform DARwIn-OP for the 

evaluation 

15:15–15:30 WeCT7.8 

Stretched knee walking with novel inverse 

kinematics for humanoid robots 

Przemyslaw Krycza 

Graduate School of Science and Engineering, Waseda University, Japan 

Kenji Hashimoto, Hideki Kondo and Aiman Omer 

Faculty of Science and Engineering, Waseda University, Japan 

Hunk-ok Lim 

Faculty of Engineering, Kanagawa University and Humanoid Research 

Institute, Waseda University, Japan 

Atsuo Takanishi 

Department of Modern Mechanical Engineering and Humanoid Research 

Institute, Waseda University, Japan 

• Structure modeled with highly redundant 

serial kinematic chains 

• Inverse kinematics (IK) problem solved 

including multiple tasks and their relative 

priorities 

• Human-like walk with heel-contact and toe 

off as well as nearly stretched knee 

phases 

• Contains IK equations description, 

simulation and on machine experiments 


–144– 

WABIAN-2R


Multirobot Planning 

Chair Noel E. Du Toit, California Inst. of Tech. 

Co-Chair Spring Berman, Harvard Univ. 

14:00–14:15 WeCT8.1 

Multiple Agent Coordination for Stochastic 

Target Interception Using MILP 

Daniel J. Stilwell and Apoorva Shende 

ECE Department, Virginia Tech, USA 

Matthew J. Bays 

ME Department, Virginia Tech, USA 

• Multi-agent coordination control for 

multiple moving target interception 

formulated as a MILP. 

• Binary variables in MILP used to indicate 

interception. 

• Gaussian uncertainty exploited to 

formulate chance interception 

constraints. Cost adaptable to the 

specific target interception problem. 

• Proposed stochastic MILP shown to be 

no more computationally expensive than 

the corresponding deterministic MILP. 

• Suitable for multi-agent systems well 

approximated by Gaussian uncertainty. 

14:30–14:45 WeCT8.3 

A Reciprocal Sampling Algorithm for 

Lightweight Distributed Multi-Robot Localization 

Amanda Prorok and Alcherio Martinoli 

Distributed Intelligent Systems and Algorithms Laboratory 

EPFL, Switzerland 

• Fully decentralized, real-time particle filter 

algorithm accommodating realistic robotic 

assumptions 

• Probabilistic robot detection model based 

on relative range and bearing measures 

• Novel collaborative reciprocal sampling 

method allows a drastic reduction in 

number of particles needed for localization 

• Experimental validation on a team of 4 

real Khepera III robots equipped with IRbased 

range and bearing modules 

Multi-robot detection model 

14:15–14:30 WeCT8.2 

Using Minimal Communication to Improve 

Decentralized Conflict Resolution 

for Non-holonomic Vehicles 

Athanasios Krontiris and Kostas E. Bekris 

Computer Sience and Engineering Department 

University of Nevada, Reno, USA 

• This work considers the problem of 

decentralized coordination between 

multiple non-holonomic vehicles, each 

navigating to a specified goal 

• Two automata are integrated 

− Lower level automaton: guarantees 


− Higher level automaton: Updates 

desired direction based on a dynamic 

priority scheme 

• Minimalistic local communication is 

employed to avoid livelocks 

• Favorable experimental comparison 


–145– 

Simulated aircraft in different modes as 

specified by the proposed method, 

and their desired direction of motion. 

The method allows one system at a time 

to make progress to its goal. 

14:45–14:50 WeCT8.4 

Localization Using Ambiguous 

Bearings from Radio Signal Strength 

Jason Derenick, Jonathan Fink, and Vijay Kumar 


• Explores the problem of localizing robots 

using ambiguous bearing estimates (πperiodic) 

drawn from a robot’s anisotropic, 

but symmetric, radiation profile. 

• Leveraging these bearings, odometry, and 

a compass, a MH-EKF is formulated that 

exploits motion to disambiguate state and 

recover formation scale. 

• Theoretical analysis shows that despite 

the combinatoric nature of the problem, 

only 2 initial hypotheses are needed. 

• Experimental results using mobile robots, 

with Zigbee transceivers, and simulation 

results for a larger system demonstrate 

the feasibility of the approach.


Multirobot Planning 

Chair Noel E. Du Toit, California Inst. of Tech. 

Co-Chair Spring Berman, Harvard Univ. 

14:50–14:55 WeCT8.5 

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15:00–15:15 WeCT8.7 

Efficient and Complete 

Centralized Multi-Robot Path Planning 

Ryan Luna and Kostas E. Bekris 

Computer Science and Engineering, University of Nevada, Reno, USA 

• Approach uses intuitive single robot primitives 

to efficiently compute multi-robot solutions 

• Computes a sequential set of paths to solve 

the path-planning problem 

• Algorithm is complete for all instances with 

two or more empty vertices 

• Method is fast and scalable to problems with 

hundreds of robots 

• Solution quality is highly competitive with 

modern coupled and decoupled methods 

• No dependence on tunable parameters or 

discretization assumptions 

Push 

2 3 

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3 

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Swap 

1 

14:55–15:00 WeCT8.6 

M*: A complete Multirobot Path Planning 

Algorithm with Performance Bounds 

Glenn Wagner and Howie Choset 


• Subdimensional expansion 

generates dynamically 

generates a low dimensional 

search space embedded in full 

configuration space 

• M* is an implementation of 

subdimensional expansion for 

graph representations of the 

configuration space 

• M* and its variants are proven to 

be complete and either cost 

optimal or bounded suboptimal 


–146– 

Plots of the percent of trials in 

which each algorithm was able to 

find a path within 5 minutes, and 

the 10’th (red), 50’th (blue) and 

90’th (green) percentile of times 

required to find solution 

15:15–15:30 WeCT8.8 

ARMO: Adaptive Road Map Optimization for 

Large Robot Teams 

Alexander Kleiner. Dali Sun and Daniel Meyer-Delius 


� Autonomous robot teams in intra logistics 

dispatching transportation tasks 

� Multi Robot Path Planning Problem 

� Automatic adaptation of the road map 

when the map or station load has changed 

� Hidden Markov Model for detecting 

changes in the map 

� Linear Integer Programming for 

optimizing the road map configuration


Visual & Multi-Sensor Calibration 

Chair Jonathan Kelly, USC 

Co-Chair Oliver Birbach, DFKI 

14:00–14:15 WeCT9.1 

Extrinsic Calibration of 

a Single Line Scanning Lidar and a Camera 

Kiho Kwak, Daniel F. Huber, Hernan Badino, and Takeo Kanade 

• We propose a robust-weighted 

extrinsic calibration algorithm 

that is implemented easily and 

has small calibration error. 

• The extrinsic parameters are 

estimated by minimizing the 

distance error between 

corresponding features into the 

image. 

• The alignment accuracy of our 

method is two times better than 

the compared state of the art 

algorithms. 


14:30–14:45 WeCT9.3 

Optimization Based IMU Camera Calibration 

Elmar Mair and Oliver Ruepp and Darius Burschka 

Department of Informatics, Technische Universtität München, Germany 

Michael Fleps and Michael Suppa 

Institute for Robotics and Mechatronics, German Aerospace Center, Germany 

• Spatial alignment estimation of IMU and 

camera by a nonlinear batch-optimization 

• Modeling of the sensors’ trajectories by Bsplines 

• No approximations and sequential data 

processing like in Kalman filters 

• Experiments on simulated and real data 

show that the approach compares 

favorably to conventional methods 

14:15–14:30 WeCT9.2 

A Novel 2.5D Pattern for Extrinsic Calibration of 

ToF and Camera Fusion System 

Jiyoung Jung, Yekeun Jeong, Jaesik Park, Hyowon Ha, 

and In-So Kweon 

Department of Electrical Engineering, KAIST, South Korea 

James Dokyoon Kim 

Samsung Advanced Institute of Technology, South Korea 

• We present an extrinsic calibration 

method to estimate the pose of a color 

camera with respect to a 3D Time-of-Flight 

camera. 

• We use a 2.5D pattern so that the correct 

correspondences are obtained for both 

color and ToF cameras. 

• We refine the intrinsic parameters of the 

ToF camera to model its projection as a 

pinhole camera model. 

• Depth constraint which restricts the depth 

measurement to lie on the pattern plane is 

also employed into LM optimization as 

well as the reprojection errors. 


–147– 

The 2.5D pattern and the checkerboard 

in color and ToF amplitude images 

A comparison of 3D rendering results 

14:45–14:50 WeCT9.4 

Rapid Development of Manifold-Based Graph Optimization 

Systems for Multi-Sensor Calibration and SLAM 

René Wagner, Oliver Birbach 

Safe and Secure Cognitive Systems, DFKI, Germany 

Udo Frese 

Safe and Secure Cognitive Systems, DFKI, Germany 

Real-Time Computer Vision Group, University of Bremen, Germany 

• The Manifold Toolkit for Matlab (MTKM) 

brings state-of-the-art generic graph 

optimization to Matlab. 

• Using MTKM, even complex multi-sensor 

cross-calibration problems can be solved 

with very little code. 

• Get MTKM and all presented calibration 

and SLAM examples as open source from 

http://openslam.org/MTK.html 

• Build upon MTKM to get your calibration 

tasks done quickly and experiment with 

SLAM in a convenient Matlab 

environment. 

MTKM applications: Hand– 

eye+stereo+IMU calibration on a 

humanoid service robot, vertical 

stereo calibration, 3D pose 

adjustment, RGB-D+accelerometer 

calibration, 2D pose adjustment, 

2D feature SLAM.


Visual & Multi-Sensor Calibration 

Chair Jonathan Kelly, USC 

Co-Chair Oliver Birbach, DFKI 

14:50–14:55 WeCT9.5 

Two-Phase Online Calibration for Infrared-based 

Inter-Robot Positioning Modules 

Sven Gowal, Amanda Prorok and Alcherio Martinoli 

DISAL, EPFL, Switzerland 

• A novel two-phase online calibration method 

is presented. 

• The combination of a Least Mean Square 

algorithm and Gaussian Process 

Regression allows accurate estimation for 

robots with limited computational power. 

• Online and on-board estimation is possible. 

• Experiments are carried out on 4 Khepera 

III robots equipped with a IR-based range 

and bearing module. 

Example of the two-phase 

calibration procedure. 

15:00–15:15 WeCT9.7 

High-Accuracy Hand-Eye Calibration 

from Motion on Manifolds 

Federico Vicentini, Nicola Pedrocchi, Matteo Malosio 

and Lorenzo Molinari Tosatti 

National Research Council CNR-ITIA, Italy 

• new hand-eye calibration procedure 

based on generation of constant 

geometrical entities 

• robot motion is programmed for tracing 

manifolds with good interpolation/fitting 

properties 

• geometrical solution with real robot 

implementation, replacing the solution of 

quadratic AX=XB equation 

• better accuracy and robustness against 

noise in hand-eye measurement 

generation of circular manifolds for 

hand-eye transform computation 

14:55–15:00 WeCT9.6 

Self Calibration of a Vision System Embedded in 

a Visual SLAM Framework 

Cyril Joly and Patrick Rives 

ARobAS Team, INRIA Sophia Antipolis-Méditerranée, France 

• Self-calibration of the 3D transformation 

between a camera and the odometry 

during visual SLAM 

• Observability analysis based on the 

study of global properties of the system 

• Parameters estimation can be computed 

when the radius of curvature of the 

trajectory changes 

• Implementation of a simultaneous SLAM 

and calibration approach based on the 

Smoothing and Mapping (SAM) algorithm 

• Validation on real data with a camera 

mounted with a non negligible rotational 

offset 


–148– 

The translation and rotation 

transformation between the 

camera and the robot has to be 

estimated 

15:15–15:30 WeCT9.8 

A Nonlinear Observer Approach for 

Concurrent Estimation of Pose, IMU Bias and 

Camera-to-IMU Rotation 

Glauco Garcia Scandaroli1 , Pascal Morin1 and Geraldo Silveira2 1 INRIA Sophia Antipolis-Méditerranée, France 

2 CTI Renato Archer – DRVC Division, Brazil 

• Novel observer designed for pose 

(orientation and position), IMU bias and 

Camera-to-IMU rotation estimation. 

• Exponential stability is obtained under 

adequate observability condition. 

• Gyroscopes can be used to detect the 

satisfaction of the observability condition. 

• Experimental results for an inertial-visual 

sensor directly exploiting the image 

intensities support the effectiveness of the 

method.

Session WeDT1 Continental Parlor 1 Wednesday, September 28, 2011, 16:00–17:30 

Human-Robot Collaboration 

Chair Tetsunari Inamura, National Inst. of Informatics 

Co-Chair Elizabeth Croft, Univ. of British Columbia 

16:00–16:15 WeDT1.1 

Enhanced Visual Scene Understanding 

through Human-Robot Dialog 

M. Johnson-Roberson, J. Bohg, B. Rasolzadeh and D. Kragic 

CVAP/CAS, KTH Stockholm, Sweden 

G. Skantze, J.Gustafson and Rolf Carlson 

TMH, KTH Stockholm, Sweden 

• Goal: Robot should correctly 

segment objects in the scene from 

themselves and from the background 

• Initial object hypotheses from 

active segmentation approach 

• Disambiguation through humanrobot 

interaction using incremental 

dialog system 

• Quantitative experimental 

evaluation on real data shows 

improved scene segmentation after 

human-robot interaction 

Top: Point Cloud of Scene. Middle: Initial 

Labeling. Bottom: Resegmented Scene 

after Human-Robot Interaction 

16:30–16:45 WeDT1.3 

Towards Safe Physical Human-Robot 

Collaboration: A Projection-based Safety System 

Christian Vogel, Maik Poggendorf, Christoph Walter 

and Norbert Elkmann 

Fraunhofer Institute for Factory Operation and Automation IFF, 

Magdeburg, Germany 

• Based on conventional projector and 

camera equipment 

• Projection of safety-spaces directly into 

the environment (e.g. onto the floor) 

• Cameras reliably detect violations 

whenever projection rays are disrupted 

• Reduced influence of changing 

environmental light conditions 

• Intrinsically safe system 

• Dynamic change of shape, size, position 

• Display additional information 

• Visible/ non-visible light 

16:15–16:30 WeDT1.2 

Intention-Based Coordination and Interface 

Design for Human-Robot Cooperative Search 

Dan Xie, Yun Lin, Roderic Grupen and Allen Hanson 


University of Massachusetts Amherst, USA 

• A multi-agent search scheme is presented 

that supports the recognition of activities 

and, thus, learning methods for 

cooperative human-robot interaction. 

• The robot updates a Probabilistic 

Distribution Function of the target object 

using the observations and the estimated 

state of human peers to compensate the 

human behaviors. 

• An implicit interface design that reduces 

the cognitive workload -- the robot infers 

the intention of the user and provides 

assistance autonomously. 

16:45–16:50 WeDT1.4 

Experimental Investigation of Human- 

Robot Cooperative Carrying 

Chris A. C. Parker and Elizabeth Croft 


• Human behavior in a human-robot 

cooperative carrying domain was 

investigated. 

• 19 non-expert human subjects 

participated in this study. 

• The importance of visual and haptic 

feedback to human users was 

examined. 

• Humans also were found to apply 

significant tension to cooperatively 

carried loads under certain conditions. 


–149– 

Experimental Setup of our 

HRI Study


Human-Robot Collaboration 

Chair Tetsunari Inamura, National Inst. of Informatics 

Co-Chair Elizabeth Croft, Univ. of British Columbia 

16:50–16:55 WeDT1.5 

A Human-Centered Approach to Robot Gesture 

Based Communication 

within Collaborative Working Processes 

T. Ende, S. Haddadin, S. Parusel, T. Wüsthoff, M. Hassenzahl, A. Albu-Schäffer 

Institute of Robotics and Mechatronics (DLR), Germany 

Folkwang University of the Arts Essen, Germany 

• Identification of relevant gestures from 

human observations for human-robot coworking 

tasks 

• Transfer of gestures to robotic systems 

of increasing abstraction 

• Evaluation of human recognition rate for 

isolated human and robotic gestures 

• Robotic gesture lexicon and machine 

• Complex use-case with single arm 

manipulators in a drug preparation 

scenario 

Transferring gestures from human observation 

to human-robot collaborative tasks 

17:00–17:15 WeDT1.7 

Motion Coaching with Emphatic Motions and 

Adverbial Expression for Humans by a Robot 

Keisuke Okuno 

Department of Informatics, SOKENDAI, Japan 

Tetsunari Inamura 

National Institute of Informatics / SOKENDAI, Japan 

• Propose a method to bind Emphatic 

Motions and Adverbial Expressions. 

• Propose a method to control degree of 

emphasis of synthesized motions and 

adverbial expressions. 

• Realize the methods by using a sole 

parameter in a phase space. 

• Show the feasibility with a motion 

coaching system and experiments of 

actual sport coaching. 

The “α “ binds and control both 

emphatic motions and adverbial 

expressions 

16:55–17:00 WeDT1.6 

Human Workflow Analysis using 3D Occupancy 

Grid Hand Tracking in a Human-Robot 

Collaboration 

Claus Lenz, Alice Sotzek, Thorsten Röder, 

Helmuth Radrich and Alois Knoll 

Robotics and Embedded Systems, Technische Universitä̈ t München, Germany 

Markus Huber and Stefan Glasauer 

Center for Sensorimotor Research Department of Neurology, 

Ludwig-Maximilians-Universität München, Germany 

• HMM-based human workflow 

analysis of an assembly task 

jointly performed by human 

and robot 

• Training of models without 

influence of a robot 

• Evaluations to find taskrelevant 

and set-up 

independent 

• Transfer of workflow analysis 

models to a robotic system 

using a new three-dimensional 

occupancy grid tracking 

approach to estimate the hand 

positions as input data 

17:15–17:30 WeDT1.8 

A system for interactive learning 

in dialogue with a tutor 

D. Skočaj, M. Kristan, A. Vrečko, M. Mahnič, M.Janíček, 

G.J. Kruijff, M. Hanheide, N. Hawes, T. Keller, M. Zillich, K. Zhou 

University of Ljubljana, DFKI Saarbrücken, University of Birmingham, 

Albert-Ludwigs-Universität Freiburg, Vienna University of Technology 

• Representations and mechanisms that 

facilitate continuous learning of visual 

concepts in dialogue with a tutor. 

• Implemented in a robotic system. 

• Beliefs about the world are 

• created by processing visual and 

linguistic information 

• used for planning system behaviour. 

• Different kinds of learning initiated by the 

human tutor or by the system itself: 

• Tutor-driven learning 

• Situated tutor-assisted learning 

• Non-situated tutor-assisted learning 


–150– 

Scenario setup and 

system overview


Recognition & Prediction of Motion 

Chair Sylvain Calinon, Italian Inst. of Tech. 

Co-Chair Sachin Chitta, Willow Garage Inc. 

16:00–16:15 WeDT2.1 

Realtime Recognition of Complex Daily 

Activities Using Dynamic Bayesian Network 

Chun Zhu and Weihua Sheng 

School of Electrical and Computer Engineering 

Oklahoma State University, USA 

• Daily complex activities are recognized 

using wearable motion sensors and 

location information 

• Adaptive gesture spotting is proposed 

to segment gestures for different 

environments and body activities 

• A dynamic Bayesian network is 

developed to recognize body activities 

and hand gestures simultaneously 

• Test in a mock apartment shows good 

recognition results 

The hardware platform 

Dynamic Bayesian network for 

complex activity recognition 

16:30–16:45 WeDT2.3 

Movement Segmentation using a 

Primitive Library 

Franziska Meier 1 , Evangelos Theodorou 1 , Freek Stulp 1 

and Stefan Schaal 1,2 

1 CLMC Lab, University of Southern California 

2 Max-Planck-Institute for Intelligent Systems, Tuebingen 

• The segmentation problem is reduced 

to online sequential movement 

recognition. 

• Original Dynamic Movement Primitives 

are reformulated to take the form of a 

linear dynamical system. 

• Based on this new formulation an EM 

algorithm is developed to estimate the 

duration and goal of 

partially observed motion. 

• Results on two applications of this new formulation - online movement 

recognition and movement segmentation – are presented. 

16:15–16:30 WeDT2.2 

Motion Data Retrieval Based on Statistic Correlation 

between Motion Symbol Space and Language 

Seiya Hamano and Wataru Takano 

Yoshihiko Nakamura 

Department of Mechano-Informatics, The University of Tokyo, Japan 

• The motion retrieval method with word 

based on Canonical Correlation Analysis 

between motion features and word 

features is proposed. 

• The motion feature is represented by the 

proto-symbol space and the word feature 

is represented by the binary feature of 

word labels. 

• This method enables motion retrieve 

considering similarity of motion pattern 

and closeness of word meaning. 

16:45–16:50 WeDT2.4 

Encoding the temporal and spatial constraints 

of a task in explicit-duration HMM 

Sylvain Calinon, Antonio Pistillo and Darwin G. Caldwell 

Advanced Robotics Dept, Italian Institute of Technology (IIT), Genova, Italy 

•Aim: Recovery from perturbations in 

physical human-robot interaction (pHRI) 

• We consider scenarios where the user 

momentary perturbs the task by physically 

holding the robot (the robot is not aware of 

the type and place of the perturbation). 

• Kinesthetic teaching is used to learn the 

task, with non-linear motion encoded as a 

weighted combination of linear systems. 

• We focus here on the design of a weighting 

mechanism that can handle various types 

of task constraints. 

• We review existing methods and introduce 

Hidden Semi-Markov Model (HSMM) as a 

way to encapsulate the importance of time 

and space constraints in a parametric form. 

• Two pHRI experiments show that different 

behaviors can be obtained depending on the 

chosen HSMM weighting schemes. 


–151–


Recognition & Prediction of Motion 

Chair Sylvain Calinon, Italian Inst. of Tech. 

Co-Chair Sachin Chitta, Willow Garage Inc. 

16:50–16:55 WeDT2.5 

Behavior Prediction from Trajectories in a House 

by Estimating Transition Model Using Stay Points 

Taketoshi Mori and Hiroshi Noguchi 

Dept. of Life Support Technology (Molten), The Univ. of Tokyo, Japan 

Shoji Tominaga, Masamichi Shimosaka, 

Rui Fukui and Tomomasa Sato 

Dept. of Mechano-Informatics, The Univ. of Tokyo, Japan 

• Prediction by preceding activities 

before the target behaviors 

• Segmentation of trajectory data into 

staying or moving and classification 

of the segments 

• Mining time-series association rules 

from transition events of each 

segmented trajectory 

• Validated by experiment using 

trajectories of approx. 2 years. 

(Upper) Room Layout 

(Lower) Example of Transition 

17:00–17:15 WeDT2.7 

Trajectory Prediction of Spinning Ball 

for Ping-Pong Player Robot 

Yanlong Huang, De Xu, Min Tan and Hu Su 

State Key Laboratory of Intelligent Control and Management of Complex 

Systems, Institute of Automation, Chinese Academy of Sciences, China 

• Analytic flying model: the flying ball’s 

self-rotational velocity is estimated 

and sufficiently taken into account in 

the model 

• Rebound Model: the relation 

between the velocities before and 

after rebound 

• Trajectory Prediction: initial trajectory 

fitting, initial velocities calculation, 

landing position prediction, rebound 

velocities calculation, striking position 

prediction 

Z(mm) 

600 

400 

200 

0 

0 

100 

200 

X(mm) 

300 

The trajectory prediction 

Predict with angular velocity 

Predict without angular velocity 

Actual 

-500 

-1000 

-1500 

-2000 

400 -2500 Y(mm) 

16:55–17:00 WeDT2.6 

Estimation and Prediction of Multiple Flying Balls 

Using Probability Hypothesis Density Filtering 

Oliver Birbach and Udo Frese 

DFKI, Bremen, Germany 

� Position and velocity estimation of flying 

balls using recently developed GM-PHD 

filter 

� Detected flying balls represented as peaks 

in a Gaussian mixture, propagated by 

UKF over time 

� Prior of typically pitched balls integrated 

into GM-PHD for trajectory starting and 

increased robustness 

� Used as tracking component in a robotic 

ball catching application 


–152– 

PHD as projection into image and 

onto the floor. 

17:15–17:30 WeDT2.8 

Invariant Trajectory Indexing for Real Time 3D 

Motion Recognition 

Jianyu Yang, Y.F. Li and Keyi Wang 

Dept of MEEM, Joint Advanced Research Center of 

University of Science and Technology of China and 

City University of Hong Kong, Suzhou, China 

• A dynamic indexing method is proposed 

for representing, retrieving and real time 

recognizing motion trajectories. 

• Trajectory is segmented into basic 

segments by classifying the points, and 

we use the labels of the basic segments 

to title the trajectory. 

• All the trajectories of database are titled 

as an indexing database, and the query 

trajectory is retrieved and recognized by 

the matching of titles. 

• As the matching of title is simple, this 

method is effective for motion retrieval 

and real time recognition. 

A model of segmented trajectory 

(a) and a real case (b).


Haptic Rendering & Object Recognition 

Chair M. Cenk Cavusoglu, Case Western Res. Univ. 

Co-Chair Masashi Konyo, Tohoku Univ. 

16:00–16:15 WeDT3.1 

A Comparison of Encoding Schemes for Haptic 

Object Recognition using a Biologically 

Plausible Spiking Neural Network 

Sivalogeswaran Ratnasingam and T.M. McGinnity 

Intelligent Systems Research Centre, School of Computing and Intelligent 

Systems, Magee campus, University of Ulster, United Kingdom 

• A biologically inspired spiking neural 

network based haptic object recognition 

system is proposed. 

• A number of different encoding schemes 

to convert the haptic measurements into 

spike train are proposed. 

• The spiking neural network was trained 

using a supervised training approach. 

• During the training, firing threshold of the 

hidden layer neurons were modified. 

• A robot hand with three fingers that have 

the same number of degrees of freedom 

as the human fingers was used for testing 

the system. 

• Test results show that the system can 

recognise 100 % of 7 different objects. 

Figure. Robot hand grasping a ball 

16:30–16:45 WeDT3.3 

Effect of Visuo-Haptic Co-location on 

3D Fitts’ Task Performance 

Michael J. Fu, Andrew D. Hershberger, Kumiko Sano, 

and M. Cenk Cavusoglu 

Electrical Engineering and Computer Science, 

Case Western Reserve University, USA 

• Goal was to characterize human 

performance of point-to-point reaching 

for physical, co-located/non-colocated 

virtual environment (VE), and rotated 

VE visualization conditions 

• Existing results on benefits of visual 

and haptic workspace co-location in VE 

are conflicting 

• Key finding was the significant 

decrease in end-point error for tasks 

performed in a co-located virtual 

environment display modality 

Experimental setup using 

Fishtank VE display 

16:15–16:30 WeDT3.2 

Study on lower back electrotactile stimulation 

characteristics for prosthetic sensory feedback 

Konstantinos Dermitzakis and Alejandro Hernandez-Arieta 

Artificial Intelligence Laboratory, University of Zurich, Switzerland 

Monika Seps 

Institute of Neuroinformatics, Swiss Federal Institute of Technology Zurich 

(ETHZ), Zurich, Switzerland 

• Prosthetic devices are in lack of rich 

sensory feedback to their user’s body 

• Such sensory feedback is important for 

the integration of artificial limbs to the 

body image 

• The lower back is a comfortable, low 

cognitive load feedback area 

• This study details the basic characteristics 

of electrical stimulation to the lower back 

• The lower back is found to be a viable 

location for providing such feedback 


–153– 

Moving sensation through 

electrical stimulation 

16:45–16:50 WeDT3.4 

Determining Object Geometry with 

Compliance and Simple Sensors 

Leif P. Jentoft and Robert D. Howe 


• Joint-angle sensors on compliant joints 

are robust and gentle 

• Methods are presented to 

• Detect finger contact with objects 

• Sweep space to determine object 

geometry and surrounding free space 

• Eliminates need for expensive sensors to 

determine object geometry for grasping in 

vision-limited settings


Haptic Rendering & Object Recognition 

Chair M. Cenk Cavusoglu, Case Western Res. Univ. 

Co-Chair Masashi Konyo, Tohoku Univ. 

16:50–16:55 WeDT3.5 

Usability of a virtual reality system based on a 

wearable haptic interface 

Ingo Kossyk and Konstantin Kondak 

Robotics and Mechatronics, German Aerospace Center (DLR), Germany 

Jonas Dörr and Lars Raschendörfer 

Robotics and Biology Laboratory, Technische Universität Berlin, Germany 

• We evaluate the usability of a wearable 

haptic interface with subjective and 

objective measures 

• A presence questionnaire with 18 test 

persons served as a subjective measure 

• A task performance experiment was 

conducted for an objective measure 

17:00–17:15 WeDT3.7 

Enhancement of Human Force Perception by 

Multi-point Tactile Stimulation 

Lope Ben Porquis, Masashi Konyo and Satoshi Tadokoro 

Graduate School of Information Science, Tohoku University, Japan 

• Multiple skin-contacts are natural 

consequence when gripping an 

object or a tool. 

• Aggregation of strain energy density 

by applying tensile and compressive 

forces. 

• Experimentally verified that multicontact 

stimulation applied on a 

tactile interface can enhance force 

perception. 

• Vacuum stimuli can be used as 

complementary stimuli for inducing 

force sensation. 

SED 

Mechanoreceptors 

Tactile 

Stimuli 

Tool Segment 

Force 

Index Finger 

Tensile 

Force 

Participant 

Force 

Weight 

Compressive 

Force 

16:55–17:00 WeDT3.6 

Teaching by Touching: Interpretation of 

Tactile Instructions for Motion Development 

Fabio DallaLibera, Fransiska Basoeki, Hiroshi Ishiguro 

Department of System Innovation, Osaka University, Japan 

Takashi Minato 

ATR Hiroshi Ishiguro Laboratory, ATR, Japan 

Emanuele Menegatti 

Department of Information Engineering, Padua University, Italy 

• Touch is an important means of 

communication among humans 

• Sport or dance instructors communicate 

much information using simple touches 

• To permit natural interaction, humanoid 

robots should interpret the meaning of 

tactile instructions 

• A prototype system for the development of 

robot motions is presented, and a 

preliminary analysis on how humans 

employ touch to express their intention is 

provided. 

17:15–17:30 WeDT3.8 

Enhancement of Vibrotactile Sensitivity: 

Effects of Stationary Boundary Contacts 

Tatsuma Sakurai, Masashi Konyo and Satoshi Tadokoro 

Graduate School of Information Scienses, Tohoku University, Japan 

• Simultaneous contact with vibratory and 

stationary surfaces enhances human 

vibrotactile sensitivity, which we call 

stationary-boundary-contact(SBC) 

enhancement. 

• SBC produces a sharp line sensation 

along the gap between the vibratory and 

stationary surfaces 

• Psychophysical experiments showed the 

detection thresholds were more than 

tripled as low at lower frequencies 

• The mechanism behind SBC 

enhancement is investigated by using a 

finite element model for the skin 


–154– 

NOT SBC 

SBC 

Stationary 

Surface 

Skin 

Stationary 

Surface 

Vibratory 

Surface 

Vibratory 

Surface 

Vibratory 

Surface 

Skin 

Impact 

Stationary 

Surface 

Vibratory 

Surface 

Schema of Stationary Boundary 

Contacts

Session WeDT4 Continental Ballroom 4 Wednesday, September 28, 2011, 16:00–17:30 

Industrial Forum: Medical Robotics 

Chair Paolo Dario, Scuola Superiore Sant'Anna 

Co-Chair 


–155–


Symposium: Robot Motion Planning: New Frameworks and High Performance 

Chair Maxim Likhachev, Carnegie Mellon Univ. 

Co-Chair Ron Alterovitz, Univ. of North Carolina at Chapel Hill 

16:00–16:15 WeDT5.1 

Multi-resolution H-Cost Motion Planning: A New 

Framework for Hierarchical Motion Planning for 

Autonomous Mobile Vehicles 

Raghvendra V. Cowlagi and Panagiotis Tsiotras 

School of Aerospace Engineering, 

Georgia Institute of Technology, Atlanta, GA, USA 

• Computationally efficient 

hierarchical motion planner that 

incorporates vehicle dynamical 

constraints 

• Higher-level: Provably complete 

multi-resolution path planner using 

wavelet transform-based cell 

decompositions 

• History-based transition costs in 

cell decomposition graph for 

including vehicle dynamical 

constraints 

• Lower-level: Local trajectory 

generation algorithm based on 

geometric constructions and 

model predictive control 

Schematic illustration of the proposed 

motion planning framework 

16:30–16:45 WeDT5.3 

Massively Parallelizing the RRT and the RRT∗ 

Joshua Bialkowski and Emilio Frazzoli 

Department of Aeronautics and Astronautics, 

Massachussetts Institute of Technology, USA 

Sertac Karaman 

Department of Electrical Enginnering and Computer Science, 

Massachussetts Institute of Technology, USA 

• GPUs have exhibited recent performance 

growth and support for general purpose 

computation 

• There is potential for significant speed-up 

in sampling based motion planning 

implementations 

• Collision checking is the primary 

bottleneck in many implementations of 

motion planning problems 

• We investigate parallel collision checking 

for a multi-link robotic manipulator using 

GPU hardware 

An N-Link Robotic 

Manipulator 

16:15–16:30 WeDT5.2 

Multi-Scale LPA* with Low Worst-Case 

Complexity Guarantees 

Yibiao Lu and Xiaoming Huo 

H. Milton Stewart School of Industrial and System Engineering 

Oktay Arslan and Panagiotis Tsiotras 

D. Guggenheim School of Aerospace Engineering 


• Combine the incremental 

search with map abstraction 

• Utilize multiscale graphical 

structure constructed via 

beamlets 

• Improve both run-time 

efficiency and robustness at 

execution level 

• Theoretical proofs on 

computational complexity 


–156– 

Replanning via m-LPA* 

16:45–16:50 WeDT5.4 

Deterministic Kinodynamic Planning 

with hardware demonstrations 

François Gaillard, Michaël Soulignac and Cédric Dinont 

CS Dept, ISEN Lille, France 

Philippe Mathieu 

SMAC-LIFL, University of Lille 1, France 

� DKP is a deterministic hybrid approach for 

trajectory planning 

� Incrementally builds a tree of spline 

trajectories 

� Guarantees on trajectory feasibility and 

reproducibility of the results 

� Explicit geometrical representation of local 

reachable space 

Reachable 

space 

Iterative planning within 

reachable spaces


Symposium: Robot Motion Planning: New Frameworks and High Performance 

Chair Maxim Likhachev, Carnegie Mellon Univ. 

Co-Chair Ron Alterovitz, Univ. of North Carolina at Chapel Hill 

16:50–16:55 WeDT5.5 

Navigation Meshes for Realistic 

Multi-Layered Environments 

Wouter van Toll, Atlas F. Cook IV, Roland Geraerts 

Utrecht University, Netherlands 

• A multi-layered environment is 

a connected set of 2D floors. 

• Examples: 

multi-storey buildings 

train stations 

airports 

shopping malls 

• We implement a multi-layered 

navigation mesh and use this 

structure to perform path 

planning operations. 

17:00–17:15 WeDT5.7 

Adaptive Time Horizon for On-line Avoidance 

in Dynamic Environments 

Zvi Shiller 1 , Oren Gal 2 and Ariel Raz 1 

Dept. Mechanical Engineering and Mechatronics, Ariel University Center, Israel 

2 Mechanical Engineering, Technion, Israel 

• On-line avoidance of static and 

moving obstacles based on Velocity 

Obstacles (VO) 

• VO truncated at an adaptive time 

horizon 

• Adaptive time horizon allows 

sufficient time for safe avoidance 

• VO conservatively but tightly 

approximates the set of inevitable 

collision states (ICS) 

• Planner generates near-time optimal 

trajectories 

16:55–17:00 WeDT5.6 

Solving Shortest Path Problems with Curvature 

Constraints Using Beamlets 

Oktay Arslan and Panagiotis Tsiotras 

D. Guggenheim School of Aerospace Engineering, 


Xiaoming Huo 

H. Milton Stewart School of Industrial and System Engineering, 


• A new multiscale graph representation of 

the environment which uses beamlets 

• Computation of paths with more feasibility 

guarantees (i.e., bounded heading angle 

on the path) 

• Searching on a larger space of curves by 

adopting the beamlet graph representation 

• Being able to compute complex curves 

(i.e., a self-intersecting path) 


–157– 

Proposed approach can find a 

self-intersecting path which may 

result due to the type of heading 

angle constraint on the path 

17:15–17:30 WeDT5.8 

Spline Templates for Fast Path Planning in 

Unstructured Environments 

Marcel Häselich, Nikolay Handzhiysky, 

Christian Winkens and Dietrich Paulus 

Computer Sciences, University of Koblenz-Landau, Germany 

AGAS robotics, robots.uni-koblenz.de 

• Perception and representation of 

unstructured environments from multiple 

sensors 

• Efficient data structure with terrain 

negotiability 

• Path planning for an autonomous 

heavyweight outdoor robot 

• Fast algorithm based on precomputed 

spline templates (arrays of terrain cells)


Symposium: Marine Robotics: Control and Planning 

Chair Fumin Zhang, Georgia Inst. of Tech. 

Co-Chair Mandar Chitre, National Univ. of Singapore 

16:00–16:15 WeDT6.1* 

Semi-Plenary Invited Talk: Applications of Marine 

Robotic Vehicles 

Junku Yuh, Korea Aerospace University 

16:30–16:45 WeDT6.3 

3D-Surface Reconstruction for Partially 

Submerged Marine Structures 

Using an Autonomous Surface Vehicle 

Georgios Papadopoulos 1 , Hanna Kurniawati 2 , Ahmed S. B. M. 

Shariff 3 , Liang Jie Wong 3 , Nicholas M. Patrikalakis 1 

1 Massachusetts Institute of Technology, USA 

2 SMART Center, Singapore 

3 National University of Singapore, Singapore 

• Proposed Robotic Platform 

• GPS denied environment 

• Surface Reconstruction 

above water-line part 

• Both above and below 

water-line parts 

16:15–16:30 WeDT6.2 

Semi-Plenary Invited Talk: Applications of Marine 

Robotic Vehicles 

Junku Yuh, Korea Aerospace University 

16:45–16:50 WeDT6.4 

Path tracking: Combined path following 

and trajectory tracking 

for autonomous underwater vehicle 

Xianbo Xiang1,2 , Lionel Lapierre2 , Chao Liu2 and Bruno Jouvencel2 1Huazhong University of Science and Technology, China 

2Department of Robotics, LIRMM/CNRS, France 

• This paper proposes a novel 

path tracking control for 

autonomous underwater 

vehicles (AUVs), which blends 

the conventional path following 

and trajectory tracking control 

in order to achieve smooth 

spatial convergence and tight 

temporal performance as well 


–158–


Symposium: Marine Robotics: Control and Planning 

Chair Fumin Zhang, Georgia Inst. of Tech. 

Co-Chair Mandar Chitre, National Univ. of Singapore 

16:50–16:55 WeDT6.5 

Autonomous Data Collection from Underwater 

Sensor Networks using Acoustic Communication 

Geoffrey A. Hollinger and Gaurav S. Sukhatme 

Computer Science Department, University of Southern California, USA 

Urbashi Mitra 

Department of Electrical Engineering, University of Southern California, USA 

• Underwater sensor networks monitor 

phenomena such as seismic activity 

and environmental conditions 

• An Autonomous Underwater Vehicle 

(AUV) equipped with an acoustic 

modem can gather data from such 

networks 

• We propose and analyze approximation 

algorithms for coordinating the AUV 

based on probabilistic variants of TSP 

• Simulations using realistic acoustic 

communication modeling demonstrate 

substantial improvement versus prior 

techniques 

Example sensor deployment on the 

ocean floor to monitor environmental 

conditions. Such sensors remain in place 

for many months, and collecting data 

from them is a challenging problem. 

17:00–17:15 WeDT6.7 

Underwater SLAM with Robocentric Trajectory 

Using a Mechanically Scanned Imaging Sonar 

Antoni Burguera, Yolanda González, Gabriel Oliver 

Dept. Matemàtiques i Informàtica, Universitat de les Illes Balears, Spain 

• Underwater MSIS scan-based 

SLAM 

• Correction of motion induced 

scan distortions 

• Explicit trajectory representation 

relative to the robot (robocentric) 

• Mean position error of 1.15m after 

more than 600m experiment 

An experiment showing 

robocentric trajectory results and 

the ground truth (DGPS) 

16:55–17:00 WeDT6.6 

Modeling and Reactive Navigation 

of an Autonomous Sailboat 

Clement Petres, Miguel Romero, Frederic Plumet 

ISIR, UPMC Univ. Paris 06, France 

Bertrand Alessandrini 

Fluid Mechanics Laboratory, Ecole Centrale de Nantes, France 

• ASAROME project: Autonomous SAiling 

Robot for Oceanographic MEasurements 

• Experimental identification of a dynamic 

model of a sailboat � realistic simulator 

• Potential field based path planning 

method � real-time reactive navigation 

• Simulation of various navigation strategies 


–159– 

ASAROME prototype 

17:15–17:30 WeDT6.8 

A Lower Bound On Navigation Error for Marine 

Robots Guided by Ocean Circulation Models 

Klementyna Szwaykowska and Fumin Zhang 

Electrical and Computer Engineering, Georgia Institute of Technology, USA 

• We analyze effectiveness of using Ocean 

General Circulation Models (OGCMs) to 

guide autonomous marine robots 

• Controlled Lagrangian Particle Tracking 

(CLPT) used to study motion of 

autonomous agents in the ocean 

• We derive lower bound on error in 

predicted robot position, which depends 

on OGCM resolution 

• We show that long-term error in predicted 

robot position has bounded growth rate 

Error in robot position prediction 

during 2006 field experiment in 

Monterey Bay, CA.


Tracking & Gait Analysis 

Chair Gian Luca Mariottini, Univ. of Texas at Arlington 

Co-Chair Domenico Prattichizzo, Istituto Italiano di Tecnologia 

16:00–16:15 WeDT7.1 

Identification of Mobile Entities Based on 

Trajectory and Shape Information 

Zeynep Yücel Tetsushi Ikeda Takahiro Miyashita Norihiro Hagita 

Advanced Telecommunications Research Institute International 

Japan 

• This study integrates motion characteristics and geometric shape 

models in object recognition from range samples. 

• The shape model is described by parameters of an elliptic fit and is 

utilized to obtain the prior probabilities. 

• The motion characteristics 

are described in terms of 

coherence quality as 

moving at the front, rear, 

side, or alone and is utilized 

to get the posterior 

probabilities. 

• The method is shown to 

yield 92% recognition rate 

in an uncontrolled field 

experiment with more than 

500 participants. 

Three entities moving coherently. e i and e j 

present side-by-side motion where e j and e k 

are in following motion. 

16:30–16:45 WeDT7.3 

Adaptive Human Shape Reconstruction via 

3D Head Tracking for Motion Capture in 

Changing Environment 

Kazuhiko Murasaki*, Masamichi Shimosaka**, 

Taketoshi Mori** and Tomomasa Sato** 

*Nippon Telegraph and Telephone Corporation, Japan 

**The University of Tokyo, Japan 

• Markerless motion capture techniques with dynamic background 

scenarios including furniture movements and light condition changes 

• Robust human silhouette reconstruction by the following 2 techniques 

• Iterative graph-cut methods with head positions 

• Novel tree-based multi-class recognition as multi-view face detectors 

16:15–16:30 WeDT7.2 

Marathoner Tracking Algorithms for a High 

Speed Mobile Robot 

Eui-Jung Jung, Byung-Ju Yi, Il Hong Suh, and Si Tae Noh 

Department of Electronics and System Engineering, Hanyag University, Korea 

Jae Hoon Lee 

Graduate School of Science and Engineering, Ehime University, Japan 

Shin’ichi Yuta 

Institute of Engineering Mechanics and Systems, University of Tsukuba, Japan 

• Tracking algorithms of a mobile robot that 

follows a human body moving at high speed 

in unstructured outdoor environment 

• The mobile robot is equipped with one laser 

range finder to perform high-speed human 

tracking and obstacle avoidance. 

• A waist part of the human body is found the 

best part of the human body for robust 

tracking by a laser range finder. 

• An obstacle avoidance algorithm 

considering the relative velocity between 

the robot and obstacles is proposed. 


–160– 

Marathoner Following Robot 

16:45–16:50 WeDT7.4 

Cooperative active target tracking for heterogeneous 

robots with application to gait monitoring 

Fabio Morbidi1 , Christopher Ray2 , Gian Luca Mariottini1 , 

1 ASTRA Robotics Laboratory, Dept. of Computer Science & Engineering 

2 Dept. of Kinesiology 

University of Texas at Arlington, USA 

• Cooperative active target-tracking for a team of heterogeneous robots 

equipped with 3-D range-finding sensors 

• Gradient-based controllers are designed for each robot and the 

Kalman-Bucy filter is used for estimation fusion 

• Robots use relative-position measurements to localize themselves in 

GPS-denied environments 

• Validation on a team of aerial and ground robots tracking the motion of 

a human subject for a gait-monitoring task


Tracking & Gait Analysis 

Chair Gian Luca Mariottini, Univ. of Texas at Arlington 

Co-Chair Domenico Prattichizzo, Istituto Italiano di Tecnologia 

16:50–16:55 WeDT7.5 

Fast Visual People Tracking using a Feature- 

Based People Detector 

Achim Königs and Dirk Schulz 

Unmanned Systems Group, 

Fraunhofer Institute for Communication, 

Information Processing and Ergonomics FKIE, Germany 

• Visual people tracking that combines 

feature-based object detection with 

feature tracking. 

• Provides a fast tracking process and 

allows to specialize to tracking a specific 

object. 

• The tracking process is based on tracking 

the features found by the detector. 

• Features are tracked by carrying out a 

correspondence search from frame to 

frame in combination with a voting 

scheme for determining a person’s center 

position in the image. 

Tracked person, distinguished 

from other similar person. White 

dots denote the tracked features. 

17:00–17:15 WeDT7.7 

Particle Filter Based Monocular Human Tracking 

with a 3D Cardbox Model and a Novel 

Deterministic Resampling Strategy 

Ziyuan Liu + , Dongheui Lee + and Wolfgang Sepp * 

+ Technische Universität München, Germany 

* German Aerospace Center, Germany 

• Particle filter based monocular human 

upper body tracking 

• A novel deterministic resampling strategy 

(DRS) 

• A new 3D articulated human upper body 

model with the name 3D cardbox model 

• Quantitative evaluation of the tracking 

results 

• Comparison between DRS and standard 

stratified resampling strategy (SRS) 

• Comparison with a commercial motion 

capturing system 

Tracking example: projected human 

model (top), silhouette matching 

(middle), raw image (bottom) 

16:55–17:00 WeDT7.6 

A Nonlinear Controller for People Guidance 

based on Omnidirectional Vision 

Flávio Garcia Pereira 

Department of Engineering and Computing, Federal University of Espírito 

Santo, Brazil. 

Milton César Paes Santos and Raquel Frizera Vassallo 

Department of Electrical Engineering, Federal University of Espírito Santo, 

Brazil. 

• Guide-tour robot; 

• Human’s movement; 

• Nonlinear final pose controller ; 

• Lyapunov analysis; 

• Omnidirectional vision; 

• Human detection. 


–161– 

A robot navigating and a person 

following it. 

17:15–17:30 WeDT7.8 

Non-Drifting Limb Angle Measurement Relative 

to the Gravitational Vector 

Andrew Petruska and Sanford Meek 


• Two accelerometers and one 

rate gyro are used measure 

inclination for feedback control 

and gait analysis. 

• The estimate of inclination from 

the two accelerometers is not 

biased by angular motion. 

• The inclination is blended with 

the angular velocity using a 

Kalman filter. 

• RMS error for both sinusoidal 

and constant velocity inputs was 

measured to be less than 5� for 

gait-like frequencies. 

A comparison between the Kalman filter 

implemented with the dynamic angle 

estimation (solid) and without (dashed).


Multirobot Coordination & Modular Robots 

Chair Kasper Stoy, Univ. of Southern Denmark 

Co-Chair Stephen L. Smith, Univ. of Waterloo 

16:00–16:15 WeDT8.1 

Modeling Mutual Capabilities in 

Heterogeneous Teams for Role Assignment 

Somchaya Liemhetcharat and Manuela Veloso 

School of Computer Science, Carnegie Mellon University, USA 

• Agents in heterogeneous teams have different capabilities. 

• These capabilities are not only individual, but depend on the 

composition of the team and the role assignments. 

• We contribute the Mutual State Capability-Based Role Assignment 

(MuSCRA) model that models pairwise interactions between agents 

and their mutual state. 

• The MuSCRA model is applicable to dynamic environments where 

state affects the performance of agents in the team. 

16:30–16:45 WeDT8.3 

Evaluation of a Power Management System for 

Heterogeneous Modules 

in Self-Reconfigurable Multi-Module Systems 

Zhuowei Wang, Florian Cordes, 

Alexander Dettmann, and Roman Szczuka 

German Research Center for Artificial Intelligence 

Robotics Innovation Center, Germany 

• Power source and diverse 

functionality are separated from each 

other and encapsulated in different 

modules 

• Homogeneous power management 

system can support more than 250W 

power consumption in an established 

multi-module system 

• Evaluation of key features of the 

power management system 

Self-reconfiguration of 

heterogeneous modular system 

supported by the power 

management system 

16:15–16:30 WeDT8.2 

Collision Avoidance for Persistent Monitoring in 

Multi-Robot Systems with Intersecting Trajectories 

Daniel E. Soltero Stephen L. Smith 

EECS, MIT, USA ECE, Waterloo, Canada 

Daniela Rus 

EECS, MIT, USA 

• Group of robots equipped with sensors with 

finite footprints persistently monitor a 

changing environment. 

• Collision and dead-lock avoidance 

procedure is developed to treat intersecting 

trajectories. 

• Analysis of the effects of developed 

procedure on the stability of the persistent 

controller. 

• Implementation on ground vehicles and 

aerial vehicles. 


–162– 

Four robots (yellow-filled circles) 

performing a persistent task where 

collision avoidance is essential 

due to intersecting paths. 

16:45–16:50 WeDT8.4 

Generalized Programming of Modular Robots 

through Kinematic Configurations 

M. Bordignon, K. Stoy, and U. Schultz 

Modular Robotics Lab, University of Southern Denmark, Denmark 

• Programmer models kinematics of 

each kind of module in the robot 

(~30 lines of geometric declarations) 

• Toolchain generates: 

– runtime support for computing 

forward kinematics 

– high-level primitives and low-level 

code for morphology-independent 

programming of robot 

– complete world descriptions for robot 

simulation 

• General programming language for 

modular robots 

MODEL 

PROGRAM


Multirobot Coordination & Modular Robots 

Chair Kasper Stoy, Univ. of Southern Denmark 

Co-Chair Stephen L. Smith, Univ. of Waterloo 

16:50–16:55 WeDT8.5 

Energy-Aware Coverage Control with 

Docking for Robot Teams 

Jason Derenick, Nathan Michael, and Vijay Kumar 


• A Centroidal Voronoi Tessellation 

(CVT)-based control law is formulated 

that leverages a weighting scheme that 

embeds an agent’s tradeoff to achieve 

its coverage mission and to maintain its 

energy reserve. 

• The controller is motivated by longterm 

persistent surveillance 

applications that may exceed the 

runtime of an agent. 

• As a robot’s energy-reserve depletes, it 

is drawn towards a docking station (for 

recharging) while still participating in 

coverage as the formation adjusts. 

• Stability of the distributed (among 

Voronoi-neighbors) control law is 

considered and simulation results are 

presented verifying the analysis. 

17:00–17:15 WeDT8.7 

Intent Inference and Strategic Escape in Multi- 

Robot Games with Physical Limitations 

Aris Valtazanos and Subramanian Ramamoorthy 

School of Informatics, University of Edinburgh, United Kingdom 

• Strategic decision-making framework for 

physical multi-robot games (e.g. robotic 

soccer) 

• Decouple sensory and strategic uncertainty 

• Sensory uncertainty: Reachable Set 

Particle Filter – combine probabilistic 

state estimation with dynamical constraints 

• Strategic uncertainty: Probabilistic intent 

templates fitted to the adversary and 

adjusted through data-driven estimation 

16:55–17:00 WeDT8.6 

Optimization of Personal Distribution 

for Evacuation Guidance based on Vector Field 

Masafumi Okada and Teruhisa Ando 

Dept. of Mechanical Sciences and Engineering, Tokyo TECH, Japan 

• This paper proposes an optimization 

method of personal distribution for 

evacuation guidance. 

• Based on the evacuation route, the 

intention of the evacuee is modeled by 

vector field. 

• The action of the operator is also modeled 

by vector field considering indicating 

direction. 

• By using the autonomous mobile robots 

and a radio controlled robot, the proposed 

method is evaluated in the quasi-human 

environment. 


–163– 

Guidance experiment with 

a radio controlled robot 

17:15–17:30 WeDT8.8 

Optimisation Model and Exact Algorithm for 

Autonomous Straddle Carrier Scheduling at 

Automated Container Terminals 

Binghuang Cai, Shoudong Huang, Dikai Liu, 

Shuai Yuan, and Gamini Dissanayake 

Centre for Autonomous Systems, University of Technology, Sydney, Australia 

Haye Lau and Daniel Pagac 

Patrick Technology Systems, Sydney, Australia 

• An optimisation model is presented for 

Autonomous Straddle Carriers Scheduling 

(ASCS) at automated container terminals. 

• An exact algorithm based on Branch-and- 

Bound-with-Column-Generation (BBCG) is 

presented for the ASCS problem solving. 

• The BBCG algorithm is compared to 

Binary-integer-Programming-with- 

Dynamic-Programming (BPDP) and 

Exhaustive-Search-with-Permutation-and- 

Combination (ESPC) algorithms. 

• Simulation results and discussions 

demonstrate the effectiveness and 

efficiency of the presented model and 

algorithm.


Calibration & Identification 

Chair Giorgio Metta, Istituto Italiano di Tecnologia (IIT) 

Co-Chair Jozsef Kovecses, McGill Univ. 

16:00–16:15 WeDT9.1 

Skin Spatial Calibration 

Using Force/Torque Measurements 

Andrea Del Prete, Lorenzo Natale, 

Francesco Nori and Giorgio Metta 

RBCS, Italian Institute of Technology, Italy 

Simone Denei, Fulvio Mastrogiovanni and Giorgio Cannata 

DIST, University of Genova, Italy 

• This paper deals with the problem of 

estimating the position of tactile elements 

(i.e. taxels) mounted on a robot body part 

• Taxel positions are estimated measuring 

contact forces/torques applied on the 

sensorized part using a F/T sensor 

mounted on the robot kinematic chain 

• Differently from previous works, this 

procedure provides not only the network 

topology, but also a metric information 

• The method has been validated with 

experiments on the iCub humanoid robot, 

leading to a final average error of about 7 

mm 

Taxel position estimations. 

As reference the edges of the 

triangular skin modules are drawn. 

16:30–16:45 WeDT9.3 

Static Calibration of the DLR Medical Robot 

MIRO, a Flexible Lightweight Robot with 

Integrated Torque Sensors 

Julian Klodmann, Rainer Konietschke, 

Alin Albu-Schäffer and Gerd Hirzinger 


• Stepwise approach to calibrate 

robots in the assembled state 

• Planning of static poses in the 

entire workspace to obtain 

convergence 

16:15–16:30 WeDT9.2 

Muscle Strength and Mass Distribution 

Identification Toward Subject-Specific 

Musculoskeletal Modeling 


DEMAR INRIA and LIRMM, France 

Gentiane Venture 

Tokyo University of Agriculture and Technology, Japan 

Ko Ayusawa, Yoshihiko Nakamura 

University of Tokyo, Japan 

• Framework toward subject-specific 

musculoskeletal modeling, 

• Mass Distribution Identification to 

improve the joint torque estimation, 

• Muscle Strength Identification to 

improve the muscle force estimation 

accuracy, 

• This first result highlights that the 

reliable muscle force estimation could 

be extracted after these identifications. 

16:45–16:50 WeDT9.4 

Simultaneous Calibration, Localization, 

and Mapping 

Rainer Kümmerle 1 , Giorgio Grisetti 2,1 , and Wolfram Burgard 1 

1 Dept. of Computer Science, University of Freiburg, Germany 

2 Dept. of Systems and Computer Science, Sapienza University of Rome, Italy 

• The calibration parameters of a mobile 

robot play a substantial role in navigation 

tasks. 

• Simultaneously estimate a map of the 

environment, the position of the on-board 

sensors, and its kinematic parameters 

• Our approach performs online estimation 

of the calibration parameters and is able 

to adapt to non-stationary changes. 

• Tested in simulation and with a wide range 

of real-world data 


–164– 

Effects of carrying a load on the 

wheel diameters and the odometry


Calibration & Identification 

Chair Giorgio Metta, Istituto Italiano di Tecnologia (IIT) 

Co-Chair Jozsef Kovecses, McGill Univ. 

16:50–16:55 WeDT9.5 

Experimental Evaluation of New Methods for 

In-Situ Calibration of Attitude and Doppler 

Sensors for Underwater Vehicle Navigation 

Giancarlo Troni and Louis L. Whitcomb 


Johns Hopkins University, USA 

• Development and in-water experimental 

evaluation of two new methods for in-situ 

calibration of the alignment rotation matrix 

between Doppler sonar velocity sensors 

and gyrocompass attitude sensors. 

• We report alignment calibration methods 

employing only internal vehicle navigation 

sensors, e.g. for velocity, acceleration, 

attitude, and depth. 

• Results from laboratory experiments 

comparing these methods to previously 

reported techniques indicate satisfactory 

performance of the proposed methods. 

JHU ROV in the Johns Hopkins 

Hydrodynamic Test Facility 

17:00–17:15 WeDT9.7 

Relative Accuracy Enhancement System 

Based on Internal Error Range Estimation for 

External Force Measurement in 

Construction Manipulator 

Mitsuhiro Kamezaki, Hiroyasu Iwata, and Shigeki Sugano 

Department of Modern Mechanical Engineering, Waseda University, Japan 

• Our framework adopts a relative accuracy 

improvement strategy without correcting the 

models for the practicality, comprising: 

• i. Quantifying the internal error range (IER) 

by using the sum of the maximal measuring 

errors of static and dynamic friction forces 

• ii. Calculating the error force vector by using 

IER to select cylinders that have less error 

• iii. Outputting the front load vector by using 

the cylinders whose error force vector is 

minimum 

• Experimental results indicate that our 

framework can enhance the relative 

accuracy of external force measurement 

Instrumented hydraulic arm 

16:55–17:00 WeDT9.6 

New Method for Global Identification 

of the Joint Drive Gains of Robots 

using a Known Payload Mass 

M. Gautier (1) and S. Briot (2) 

(1) University of Nantes, IRCCyN, France 

(2) CNRS, IRCCyN, France 

• Off-line identification of robot dynamic parameters 

• Inverse Dynamic Identification Model and 

LS technique (IDIM-LS) 

• Identification of the total joint drive gains in one 

step 

• Using only the weighed mass of a payload, no 

need CAD values 

• Using motor current reference, joint position, 

available robot’s controller data 

• Mixing data from trajectory without load + 

trajectory with load 

• Validation experiment, 4 first joints of a 6 dof 

industrial robot 


–165– 

FStaubli RX90 robot 

17:15–17:30 WeDT9.8 

Recursive State-Parameter Estimation 

of Haptic Robotic Systems 

Arash Mohtat, Kamran Ghaffari Toiserkan, 

and József Kövecses 

Centre for Intelligent Machines, McGill University, Canada 

• Simultaneous state-parameter estimation 

via unscented Kalman filtering (UKF). 

• Overcoming limitations: linearity of the 

models in the parameters; need for 

velocities and accelerations; and, need for 

differentiation and linearization. 

• The recursive nature allows for offline 

processing and online implementation. 

• Haptic Applications: State-parameter 

estimation of the haptic device; 

Environmental parameters identification. 

The identification experiment: 

The haptic device interacting with 

a virtual box.


–166–

8:00-9:30 



–167– 

Thursday 


Session ThAT1 ⎯⎯ Force and Stiffness Control 

Session ThAT2 ⎯⎯ Range and RGB-D Sensing 

Session ThAT3 ⎯⎯ Discrete & Kinodynamic Planning 

Session ThAT4 ⎯⎯ Symposium: Stochasticity in Robotics and Biological Systems I 

Session ThAT5 ⎯⎯ Symposium: Humanoid Robotics and Biped Locomotion 

Session ThAT6 ⎯⎯ Symposium: Robots that Can See I 

Session ThAT7 ⎯⎯ Mechanisms: Joint Design 

Session ThAT8 ⎯⎯ Autonomous Vehicles 

Session ThAT9 ⎯⎯ Towards Anthropomimetic Robots 

10:00-11:30 

Session ThBT1 ⎯⎯ Control for Manipulation & Grasping 

Session ThBT2 ⎯⎯ Mapping 

Session ThBT3 ⎯⎯ Randomized Planning & Kinematic Control 

Session ThBT4 ⎯⎯ Symposium: Stochasticity in Robotics and Biological Systems II 

Session ThBT5 ⎯⎯ Symposium: Humanoid Technologies 

Session ThBT6 ⎯⎯ Symposium: Robots that Can See II 

Session ThBT7 ⎯⎯ Medical Robots & Systems 

Session ThBT8 ⎯⎯ Search & Rescue Robots 

Session ThBT9 ⎯⎯ Intelligent Transportation Systems (Automotive) 


14:45-16:15 


–168– 

Thursday 


(continued) 

Session ThCT1 ⎯⎯ Manipulation 

Session ThCT2 ⎯⎯ Industrial Robots 

Session ThCT3 ⎯⎯ Marine Systems I 

Session ThCT4 ⎯⎯ Symposium: (Self-)assembly from the Nano to the Macro Scale: 

State of the Art and Future Directions 

Session ThCT5 ⎯⎯ Symposium: Humanoid Applications 

Session ThCT6 ⎯⎯ Symposium: Computer Vision for Robotics 

Session ThCT7 ⎯⎯ Exoskeleton Robots & Gait Rehabilitation 

Session ThCT8 ⎯⎯ Aerial Robots: Navigation, Tracking & Landing 

Session ThCT9 ⎯⎯ Swarms and Flocks 

16:45-17:45 

Session ThDT1 ⎯⎯ Forum: On Robotics Conferences: A Town Hall Meeting 

(in Continental Ballroom) 

Session ThDT3 ⎯⎯ Marine Systems II 

Session ThDT4 ⎯⎯ Novel System Designs: Locomotion (in Continental Parlor 2) 

Session ThDT5 ⎯⎯ Climbing & Brachiation (in Continental Parlor 1) 

Session ThDT6 ⎯⎯ Novel System Designs: Sensing and Manipulation 

(in Continental Parlor 9) 

Session ThDT7 ⎯⎯ Medical Robotics: Motion Planning & State Estimation 

Session ThDT8 ⎯⎯ Aerial Robots

Session ThAT1 Continental Parlor 1 Thursday, September 29, 2011, 08:00–09:30 

Force and Stiffness Control 

Chair Robert James Webster III, Vanderbilt Univ. 

Co-Chair Jonathan Hurst, Oregon State Univ. 

08:00–08:15 ThAT1.1 

Trajectory Tracking Controller With Dynamic 

Gains for Mobile Robots 

Cassius Resende 

Dept. of Electrical Engineering, Federal University of Espírito Santo, Brazil 

Felipe Espinosa, Ignacio Bravo 

Dept. of Electronics, University of Alcalá, Spain 

Mário Sarcinelli-Filho, Teodiano F. Bastos-Filho 

Dept. of Electrical Engineering, Federal University of Espírito Santo, Brazil 

• A Takashi-Sugeno fuzzy controller is 

proposed to guide a unicycle mobile 

robot during trajectory tracking 

• Stability of the closed loop system 

using such controller is proven, based 

on Lyapunov’s theory 

• The low complexity of the proposed 

controller makes it suitable for 

implementation in low-profile 

processors 

• Theoretical analysis and experimental 

results validate the proposed controller 

08:30–08:45 ThAT1.3 

Force Control for 

Planar Spring-Mass Running 

Devin Koepl and Jonathan Hurst 

Dynamic Robotics Laboratory, Oregon State University, USA 

• Presents a novel control 

strategy for spring-leg robot 

running. 

• Strategy makes good use of 

passive dynamics, conserving 

energy. 

• Incorporates active force control 

for robustness to disturbances. 

• Requires no external sensing, 

so practical for real world springleg 

robots. 

Actuated spring-leg model, with 

series hip and leg elasticity. 

08:15–08:30 ThAT1.2 

Multi-Priority Control in Redundant Robotic Systems 

Hamid Sadeghian †, Luigi Villani ††, Mehdi Keshmiri †, 

Bruno Siciliano †† 

†Mechanical Eng. Dept., Isfahan Univ. of Tech.,Iran 

††DIS, Univ. di Napoli Federico II, Italy 

• Dynamic level multi-priority control algorithm 

as a general framework for the whole body 

control of robotic systems is presented. 

• Some of the previously developed results 

are easily formalized using this general 

framework. 

• Null-space impedance control is proposed 

as one of the main results and is evaluated 

by means of computer simulation. 

08:45–08:50 ThAT1.4 

Deflection-Based Force Sensing 

for Continuum Robots: 

A Probabilistic Approach 

D. Caleb Rucker and Robert J. Webster III 

Department of Mechanical Engineering, Vanderbilt University, USA 

Medical & Electromechanical Design Laboratory 

• Flexible Continuum Robots Can Be Used As Force Sensors 

• Pose Measurements + Mechanics-Based Model => Force Estimate 

• Extended Kalman Filter Used to Quantify Uncertainty in Estimates 


–169–


Force and Stiffness Control 

Chair Robert James Webster III, Vanderbilt Univ. 

Co-Chair Jonathan Hurst, Oregon State Univ. 

08:50–08:55 ThAT1.5 

Optimality Principles in Stiffness Control 

The VSA Hammer 

Manolo Garabini † , Felipe Belo † , Andrea Passaglia † , 

Paolo Salaris † † ‡ 

, and Antonio Bicchi 

† 

Interdept. Research Center “E. Piaggio”, University of Pisa, Italy 

‡ 

Italian Institute of Technology, Genova, Italy 

• Importance of Variable Stiffness Actuators 

(VSA) in safety and performance of robots 

• Optimality principles regulating variation of 

stiffness in very dynamic tasks where 

impacts are maximized 

• New optimization problem: maximize 

velocity of a link at a given final position, 

e.g., for best hammer impact 

• Fixed stiffness case: there exists an optimal linear spring for given link and 

motor inertia 

• VSA case: varying spring stiffness during execution improves final 

performance of hammering task 

• Optimal control law obtained analytically and validated with experimental 

tests 

09:00–09:15 ThAT1.7 

Optimal and Fault-Tolerant Torque Control of Servo 

Motors Subject to Voltage and Current Limits 

Farhad Aghili 

Canadian Space Agency (CSA), Canada 

• Optimal management of motor 

excitation currents can significantly 

increase the rated speed and 

torque of the motor in the face of 

the voltage and current limits of 

the drivers. 

• In the case of one phase failure, 

controller optimally reshapes the 

stator currents of the remaining 

phases for continuing accurate 

torque production 

• A closed-form solution for the 

optimal phase currents rendering 

the control algorithm suitable for 

real-time implementation 

• Experimental results 

The architecture of the optimal 

and fault-tolerant torque 

controller 

08:55–09:00 ThAT1.6 

A novel stiffness node controller which enables 

simultaneous regulation of torque and stiffness 

in multi-muscle driven joints 

Salvatore Annunziata, Jan Paskarbeit and Axel Schneider 

Faculty of Technology, University of Bielefeld, Germany 

• Co-activation of two antagonistic 

muscles is in general considered to 

vary stiffness at joint level. 

• However, angular joint positions may 

exist for which the stiffness does not 

vary with co-activation. 

• At those positions any concurrent 

torque/stiffness controller is bound to 

fail. 

• A four-muscle based controller is 

presented that enables torque/stiffness 

control also at these positions. 


–170– 

(a) 4-muscle driven joint 

(b) Torque/angle muscle setup 

(c) Stiffness node 

09:15–09:30 ThAT1.8 

Online Motion Selection for Semi-Optimal 

Stabilization using Reverse-Time Tree 

Chyon Hae Kim and Hiroshi Tsujino 

Honda Research Institute, Japan 

Shigeki Sugano 

Graduate School of Creative Science 

and Engineering Waseda University, Japan 

• We discuss a general method for 

creating an approximately optimal 

stabilization system. 

• The proposed system creates a 

reverse-time tree using RApid Semioptimal 

MOtion-planning (RASMO). 

• We developed an online motion 

selection method that selects a 

semi-optimal motion according to the 

current state of a machine. 

• Calculation speed and optimality are 

validated in a simulation. 

Offline Process: Creating reverse-time tree by RASMO 

Input Goal States 

Search States 

Prune Paths 

Is the Tree Depth Sufficient? 

Get Reverse-Time Tree 

Online Process:� Path selection and feedback control 

Yes No 

The proposed system 

Constraints 

Resolver


Range and RGB-D Sensing 

Chair Robert Zlot, CSIRO 

Co-Chair Kai Oliver Arras, Univ. of Freiburg 

08:00–08:15 ThAT2.1 

3D Object Recognition in Range Images Using 

Visibility Context 

Eunyoung Kim and Gerard Medioni 

Computer Science Department, University of Southern California, USA 

• Goal 

• Recognize and localize queried objects 

in cluttered environments using the 

Primesensor 

• Only utilize depth information for robots 

to work under bad or no illumination 

• Contributions 

• Propose a new point pair feature using 

visibility context that contains 

discriminative description 

• Improve an pose estimation method 

using point pair matches 

• Outperform two state-of-the-art methods 

in terms of recall & precision rate 

08:30–08:45 ThAT2.3 

Comparative Evaluation of Range Sensing 

Technologies for Underground Void Modeling 

Uland Wong, Aaron Morris, Colin Lea, James Lee, 

Chuck Whittaker, Ben Garney and Red Whittaker 


• 10 sensors evaluated across three 

underground tunnel environments 

and one lab environment 

• Technologies assessed include 5 ToF 

LIDAR sensors, flash LIDAR, FMCW, 

stereo vision, the Kinect and others. 

• Metrics of evaluation inspired by 3D 

modeling performance in field 

(density distribution, accuracy, 

coverage) 

• Test environments and methodology 

are sufficiently general for 

applicability of results in related 

domains 

Sensor modeling is performed in three 

representative tunnel environments. 

08:15–08:30 ThAT2.2 

Fast Plane Extraction in 3D Range Data 

Based on Line Segments 

Kristiyan Georgiev, Ross Creed, Rolf Lakaemper 

Department of Computer and Information Sciences 

Temple University, USA 

• Extract line segments from raw data. 

• Determine connected components, 

representing possible candidate sets of 

coplanar segments. 

• Perform region growing on candidate sets 

to establish planes from coplanar 

segments. 

• Region growing contains a fast O(1) plane 

update technique in its core, leading to a 

real time performance of the algorithm. 

08:45–08:50 ThAT2.4 

Tracking a Depth Camera: Parameter Exploration 

for Fast ICP 

François Pomerleau, Stéphane Magnenat, Francis Colas, 

Ming Liu, and Roland Siegwart 

Department of Mechanical and Process Engineering, ETH Zurich, Switzerland 

• Modular ICP library openly 

available 

• Test datasets available with 

millimeter accuracy ground truth 

• Capability of 3D pose tracking at 

30 Hz with reasonable precision 

• Sound statistical evaluation over 

a range of parameters 

• Library accessible to embedded 

systems 

One path of depth camera tracked (red) with ICP at 

30 Hz vs. the measured ground truth (green) 

• Standalone C++ library: 

http://github.com/ethz-asl/libpointmatcher 

• ROS node: 

www.ros.org/wiki/modular_cloud_matcher 


–171–


Range and RGB-D Sensing 

Chair Robert Zlot, CSIRO 

Co-Chair Kai Oliver Arras, Univ. of Freiburg 

08:50–08:55 ThAT2.5 

Watertight Surface Reconstruction of Caves 

from 3D Laser Data 

Claude Holenstein, Robert Zlot, and Michael Bosse 

Autonomous Systems Laboratory, ICT Centre 

CSIRO, Australia 

• Watertight surface reconstruction of 

caves and other closed environments 

from data collected on a mobile sensor 

• Developed algorithm is scalable 

(kilometers of cave data), and able to 

remove returns from dynamic 

obstacles occluding sensor field of 

view 

• High resolution surface models have 

been produced for further scientific 

study 

Surface reconstruction of a section of 

Chifley cave. Returns from people on 

the mapping team are circled in red. 

09:00–09:15 ThAT2.7 

People Tracking in RGB-D Data With 

On-line Boosted Target Models 

Matthias Luber, Luciano Spinello and Kai O. Arras 


• We present a 3D people tracking 

approach in RGB-D data 

• Using on-line learning, we train targetspecific 

models with three types of 

RGB-D features and a confidence 

maximization search in 3D space 

• Integration into multi-hypothesis tracker 

with track interpretation feedback 

to avoid drift of on-line detectors 

and to fill gaps of misdetections 

• Results demonstrate clearly improved 

tracking performance: reduction of false 

negative tracks by 50% and reduction of 

track confusions by 24% 

3D trajectories of 

six persons 

08:55–09:00 ThAT2.6 

People Detection in RGB-D Data 

Luciano Spinello and Kai O. Arras 


• We present Combo-HOD, a fast and 

robust RGB-D people detector 

• Probabilistic fusion of 

Histogram of Oriented Gradients (HOG) 

and Histogram of Oriented Depths (HOD) 

• Uses a 3-fold accelerated depthinformed 

scale space search 

• In a comprehensive comparison with 

alternative methods, Combo-HOD 

outperforms all others with a 13% EER 

increase over vision-only HOG 

• 85% EER in a range up to 8m from 

sensor (Kinect) 

• GPU implementation runs at 30 Hz 

09:15–09:30 ThAT2.8 

‘Misspelled’ Visual Words in Unsupervised 

Range Data Classification 

Michael Firman and Simon Julier 

Department of Computer Science, University College London, UK 

• LiDAR scans can be affected by 

significant noise, which varies in 

different situations 

• We explore the effect noise has 

on the performance of 

unsupervised classifiers, using 

different local features 

The decrease in classification accuracy as noise is added to an 

artificial dataset; we also used real LiDAR data 


–172– 

• Topic models are found to be 

less robust classifiers than 

k-means when noise is added 

• Spin images are shown to be 

more robust to noise than their 

angular derivative


Discrete & Kinodynamic Planning 

Chair Edwin Olson, Univ. of Michigan 

Co-Chair Luigi Biagiotti, Univ. of Modena and Reggio Emilia 

08:00–08:15 ThAT3.1 

Improved Hierarchical Planner Performance 

Using Local Path Equivalence 

Ross A. Knepper and Matthew T. Mason 


• Motion planners face two types 

of choice: discrete decisions, 

and continuous optimization 

problems. 

• Using an equivalence relation 

among local paths, we separate 

the two types. 

• We classify path sets according 

to route among obstacles. 

• 2-stage path selection process: 

1) Select a route (set of paths) 

progressing toward the goal, and 

2) Execute the optimal (safest) 

path within the class. 

Discrete Continuous 

decision optimization 

08:30–08:45 ThAT3.3 

Choosing Landmarks for Risky Planning 

Elizabeth Murphy and Peter Corke 

School of Engineering Systems, QUT, Australia 

Paul Newman 

Mobile Robotics Group, Oxford University, UK 

• Risky Planning is a path planning 

technique applicable to probabilistic 

costmaps that achieves 70% efficiency 

increases over normal heuristic search 

methods. 

• The risk of not returning the shortest path 

is quantified and used to leverage 

planning speed ups. 

• Risky Planning requires the 

precomputation of heuristics for A* type 

searches which use landmarks to 

precompute an informative lower bound 

between any two cells in the costmap 

• This paper recommends strategies for 

placing these landmarks so that risk 

bounds of risky planning are accurate and 

precomputation is minimized. 

An overhead image of an area to 

be planned over, and some 

strategically placed landmarks 

08:15–08:30 ThAT3.2 

Simplicial Dijkstra and A* Algorithms for 

Optimal Feedback Planning 

Dmitry S. Yershov and Steven M. LaValle 

Department of Computer Science, U of Illinois at Urbana-Champaign, USA 

• Considered Euclidean shortest path 

problem in n-dimensional configuration 

space among obstacles 

• Adaptation of Dijkstra’s and A* algorithms 

to compute approximate cost-to-go 

function over simplicial complex 

• Numerical methods are carefully 

designed, analyzed, and proven to 

converge 

• Methods are demonstrated to work for 2D 

and 3D examples. Test results show 

significant improvement of Simplicial A* 

over Simplicial Dijkstra’s 


–173– 

Level sets of cost-to-go function 

in 2D and 3D environments 

08:45–08:50 ThAT3.4 

A Graph Traversal based Algorithm for Obstacle 

Detection using Lidar or Stereo 

Sujit Kuthirummal, Aveek Das, and Supun Samarasekera 

Vision and Robotics Lab, SRI International Sarnoff, USA 

• A computationally efficient approach to 

obstacle detection (OD) that is applicable 

to both structured and unstructured 

environments. 

• Instead of explicitly identifying obstacles, 

we explicitly detect scene regions that are 

traversable – safe for the robot to go to – 

from the current position. 

• The algorithm does not make any flatworld 

assumptions or need sensor pitchroll 

compensation . It also accounts for 

overhanging structures. 

• We demonstrate our approach using both 

lidar and stereo sensors. 

Top: Lidar based OD 

Bottom: Stereo based OD


Discrete & Kinodynamic Planning 

Chair Edwin Olson, Univ. of Michigan 

Co-Chair Luigi Biagiotti, Univ. of Modena and Reggio Emilia 

08:50–08:55 ThAT3.5 

Iterative Path Optimization for 

Practical Robot Planning 

Andrew Richardson and Edwin Olson 

Computer Science & Engineering, University of Michigan, USA 

• Two-stage path planning method to 

decouple path clearance from 

minimum distance path computation 

• Avoids some potential field pitfalls with 

partially-observed obstacle maps 

• Iterative optimization to maximize 

clearance (up to bound) and 

smoothness 

• Algorithm tested extensively on our 

indoor/outdoor 14 robot testbed 

Smoothed path (blue) maximizes 

clearance at A but not B, as 

clearance is sufficient. Original path 

in orange 

09:00–09:15 ThAT3.7 

Geometric Maneuverability 

with Applications to Low Reynolds Number Swimming 

Ross L. Hatton and Howie Choset 

Robotics Institute, CMU, USA 

Lisa J. Burton and A. E. Hosoi 

Department of Mechanical Engineering, MIT, USA 

• Locomoting systems use shape changes, 

rather than vector thrust, to move through 

their environment 

• We introduce a geometric framework for 

describing their maneuverability 

• Local maneuverability is similar to 

manipulability for robot arms and is 

described by an ellipsoid 

• Cyclic maneuverability characterizes 

bulk motion over gait or stroke cycles and 

is described by a polygon 

• Examples are provided for low Reynolds 

number swimmers 

08:55–09:00 ThAT3.6 

Analysis of the Discontinuities in Prioritized 

Tasks-Space Control Under Discreet Task 

Scheduling Operations 

François Keith and Pierre-Brice Wieber 

INRIA Rhône-Alpes, France 

Nicolas Mansard 

CNRS-LAAS, France 

Abderrahmane Kheddar 

CNRS-UM2 LIRMM, France – CNRS-AIST JRL, UMI3218/CRT Japan 

• Presentation of the methods defining 

a control law by a hierarchy between 

tasks and their continuity properties. 

• Illustration of their discontinuous 

behavior during discrete events such 

as task insertion, removal, swap. 

• Definition of a method ensuring a 

continuous control law even during 

these events. 

• Experimentation on HRP-2 robot. 

09:15–09:30 ThAT3.8 

Input Shaping via B-spline Filters 

for 3-D Trajectory Planning 

Luigi Biagiotti 

Department of Information Engineering 

University of Modena and Reggio Emilia, Italy 

Claudio Melchiorri 

Department of Electronics, Informatics and Systems 

University of Bologna, Italy 

• Uniform B-splines of degree p are 

equivalent to the output of a chain 

composed by p average filters 

• The frequency spectrum of B-spline 

trajectories is completely determined 

by the degree p and by time period 

T between the knots 

• These parameters are selected with the purpose of suppressing residual 

vibrations, that may be present because elastic phenomena affecting the 

robotic system 

• The effectiveness of the proposed approach is proved by considering a 

cartesian robot with elastic joints 


–174–

Session ThAT4 Continental Ballroom 4 Thursday, September 29, 2011, 08:00–09:30 

Symposium: Stochasticity in Robotics and Biological Systems I 

Chair M. Ani Hsieh, Drexel Univ. 

Co-Chair Harry Asada, MIT 

08:00–08:15 ThAT4.1* 

Semi-Plenary Invited Talk: Set-based Control in 

Stochastic Dynamical Systems: Making Almost 

Invariant Sets more Invariant 

Ira Schwartz, US Naval Research Laboratory 

08:30–08:45 ThAT4.3 

Noise, Bifurcations, and Modeling of Interacting 

Particle Systems 

Luis Mier-y-Teran-Romero and Ira B. Schwartz 

Nonlinear Systems Dynamics Section, Naval Research Laboratory, USA 

Eric Forgoston 

Department of Mathematical Sciences, Montclair State University, USA 

• Modeled self-propelling particles 

interacting through pairwise force in 

presence of noise and time delay. 

• Identified parameter regions resulting in 

different coherent structures of the swarm. 

• Showed how stochasticity modifies the 

coherent structures and attractor 

sensitivity. 

Time snapshots of transitioning 

swarms 

08:15–08:30 ThAT4.2 

Semi-Plenary Invited Talk: Set-based Control in 

Stochastic Dynamical Systems: Making Almost 

Invariant Sets more Invariant 

Ira Schwartz, US Naval Research Laboratory 

08:45–08:50 ThAT4.4 

Probability of success in stochastic robot 

navigation with state feedback 

Shridhar Shah 

Department of Mechanical Engineering, University of Delaware, USA 

Chetan Pahlajani 

Department of Mathematical Sciences, University of Delaware, USA 

Herbert Tanner 

Department of Mechanical Engineering, University of Delaware, USA 

• Robots with stochastic dynamics need 

to navigate in constrained 

environments. 

• Collision avoidance cannot be 

guaranteed with bounded inputs. 

• A deterministic, bounded input 

controller has a given probability of 

success in steering to target region, 

under stochastic uncertainty. 

• Explicit analytic solutions possible for 

single integrator point robots in a 

constrained, spherical but obstaclefree 

environments. 


–175– 

Two sample paths for stochastic 

navigation using gradient 

descent. The green path reaches 

the goal while blue path hits the 

obstacle boundary.


Symposium: Stochasticity in Robotics and Biological Systems I 


Co-Chair Harry Asada, MIT 

08:50–08:55 ThAT4.5 

A Stochastic Approach to Dubins Feedback 

Control for Target Tracking 

Ross Anderson and Dejan Milutinović 

Applied Mathematics&Statistics, University of California, Santa Cruz, USA 

• Motivated by an Unmanned Aerial Vehicle 

maintaining a nominal distance from an 

unpredictable target 

• Target is assumed to perform a random 

walk 

• Dyn. Programming on a Markov chain that 

is consistent with stochastic kinematics 

produces a feedback controller that can 

also be applied for deterministic targets 

• Effect of the level of uncertainty of target 

motion on the control law is examined 

Dubins vehicle tracking 

a random walk 

09:00–09:15 ThAT4.7 

Stochastic Tracking of Migrating Live Cells 

Interacting with 3D Gel Environment Using 

Augmented-Space Particle Filters 

Lee-Ling Ong 1 , Levi Wood 3 , Marcelo Ang Jr. 2, H.Harry Asada 1,3 

1 Singapore-MIT Alliance for Research and Technology, Singapore 

2 Department of Mechanical Engineering, NUS, Singapore 

3 Department of Mechanical Engineering, MIT, Cambridge, MA, USA 

• Types of images obtained at discrete 

time steps using confocal microscopy of 

3D cell migration experiments: 

a) 3D images of stained cell nuclei and 

b) 2D images of the gel 

• Our approach is to track a joint state 

representation, using the concepts of 


(SLAM) 

• This allows mathematically consistent 

updates from both sources and 

augmentation of new cells and conduit 

slices to the state 

Bottom figure shows visualization 

of the extracted conduit and cells 

using IMARIS (from Bitplane) 

08:55–09:00 ThAT4.6 

Optimization of Stochastic Strategies for 

Spatially Inhomogeneous Robot Swarms: 

A Case Study in Commercial Pollination 

Spring Berman and Radhika Nagpal 

Dept. of Computer Science, Harvard University, USA 

Ádám Halász 

Dept. of Mathematics,West Virginia University, USA 

• Scalable approach to optimizing 

decentralized robot control policies that 

produce a target collective behavior 

• Can maintain system performance by 

incorporating robot feedback that relies 

on coarse-grained localization 

• Swarm abstracted to linear ODE model 

whose parameters are optimized and 

mapped onto robot controllers 

• Application: Achieve uniform and 

nonuniform pollination of a field with a 

swarm of robotic bees 

09:15–09:30 ThAT4.8 

Stochastic Dynamics of Bacteria Propelled 

Spherical Micro-Robots 

Veaceslav Arabagi*, Bahareh Behkam**, and Metin Sitti* 

*Dept. of Mechanical Engineering, Carnegie Mellon University, USA 

**Dept. of Mechanical Engineering, Virginia Tech, USA 

• Stochastic dynamic model 

of bacteria propelled 

micro-beads 

• Stokes flow superposition 

of flow fields 

• Investigate stiffness of 

bacteria flagellar hook 

through simulations 

• Bead radius sensitivity 

• Number of attached 

bacteria sensitivity 

• Studying the long time 

random walk behavior of 

micro-beads 


–176–


Symposium: Humanoid Robotics and Biped Locomotion 

Chair Ambarish Goswami, Honda Res. Inst. 

Co-Chair Seung-kook Yun, Honda Res. Inst. 

08:00–08:15 ThAT5.1* 

Semi-Plenary Invited Talk: Angular Momentum-based 

Humanoid Robot Control 

Shuuji Kajita, National Inst. of AIST 

08:30–08:45 ThAT5.3 

Momentum-Based Reactive Stepping Controller on 

Level and Non-level Ground for Humanoid Robot 

Push Recovery 

Seung-kook Yun and Ambarish Goswami 

Honda Research Institute, USA 

• We present a momentum-based reactive 

stepping controller for humanoid robot 

push recovery 

• Regulation of combinations of linear and 

angular momenta allows the controller to 

selectively encourage the robot to recover 

its balance with or without taking a step 

• A reference stepping location called 

Generalized Foot Placement Estimator 

(GFPE), is computed by modeling the 

humanoid as a passive rimless wheel with 

two spokes 

• GFPE is calculated only once and it does 

not move 

• The approach works for both level and 

non-level grounds 

Humanoid takes a downhill step 

after a forward push 

08:15–08:30 ThAT5.2 

Semi-Plenary Invited Talk: Angular Momentum-based 

Humanoid Robot Control 

Shuuji Kajita, National Inst. of AIST 

08:45–08:50 ThAT5.4 

Time-Independent, Spatial 

Human Coordination For Humanoids 

Jean-Christophe PALYART LAMARCHE 1 , Olivier BRUNEAU 2 

and Jean-Guy FONTAINE 1 

1 Italian Institute of Technology (IIT), Italy 

2 Laboratoire d’Ingénierie des Systèmes de Versailles (LISV), France 

• Methodology to parameterize movements 

with 12 Cartesian gait descriptors, and 

one unique gait parameter 

• Gait coordination for kinematic simulation 

of an anthropomorphic and NAO virtual 

models 

• Real NAO robot walking 


–177–


Symposium: Humanoid Robotics and Biped Locomotion 

Chair Ambarish Goswami, Honda Res. Inst. 

Co-Chair Seung-kook Yun, Honda Res. Inst. 

08:50–08:55 ThAT5.5 

The Effect of Swing Leg Retraction on 

Running Energy Efficiency 

Matt Haberland and Sangbae Kim 

Dept. of Mechanical Engineering, Massachusetts Institute of Technology, USA 

J. G. Daniël Karssen and Martijn Wisse 

Fac. of Mechanical Engineering, Delft University of Technology, The 

Netherlands 

• Swing leg retraction (SLR): 

rearward rotation of airborne front 

leg before touchdown 

• SLR significantly improves 

energetic efficiency of prismaticlegged 

running robots 

• SLR reduces touchdown energy 

loss, impact forces, and foot 

slippage simultaneously 

• SLR is recommended as an element 

of any control strategy for running 

robots 

Model consists of point mass body, distributed mass legs, 

torque actuator, massless springs, and point feet 

09:00–09:15 ThAT5.7 

Angular Momentum: Insights into Walking and 

Its Control 

Bradford C. Bennett 

Orthopaedic Surgery, University of Virginia, USA 

Thomas Robert 

IFSTTAR, Université de Lyon, France 

Shawn D. Russell 

Mechanical and Aerospace Engineering, University of Virginia, USA 

• Nondimensional angular 

momentum organization was 

independent of walking speed. 

• The uncontrolled manifold (UCM) 

hypothesis revealed a control 

synergy for some angular 

momenta. 

• Control of bipedal robots may 

benefit from simulating the 

organization of human gait 

X 

Y 

Z 

0.02 

0 

-0.02 

0.02 

0 

-0.02 

0.02 

0 

Slow 

CWS 

Fast 

-0.02 

0 20 40 60 80 100 

Gait Cycle (%) 

The whole body normalized angular 

momentum during a gait cycle for slow, 

comfortable, and fast walking. 

08:55–09:00 ThAT5.6 

Practical Bipedal Walking Control 

on Uneven Terrain 

Using Surface Learning and Push Recovery 

Seung-Joon Yi, Daniel D. Lee 

ESE, University of Pennsylvania, USA 

Byoung-Tak Zhang 

CSE, Seoul National University, Korea 

Dennis Hong 

ME, Virginia Tech, USA 

• Bipedal walking in human environment is 

made difficult by two sources of error: the 

surface unevenness and external 

disturbances. 

• Electrically compliant swing foot and 

onboard sensors are used to estimate the 

current surface gradient, and an online 

learning algorithm is used to learn the 

surface model from noisy estimates 

• Biomechanically motivated hierarchical 

push recovery controller is used to reject 

external disturbances 

• Implemented on the DARwIn-OP 

commercial humanoid robot 

09:15–09:30 ThAT5.8 

XoR: Hybrid Drive Exoskeleton Robot 

That Can Balance 

Sang-Ho Hyon, Jun Morimoto, Takamitsu Matsubara, 

Tomoyuki Noda and Mitsuo Kawato 

Computational Neuroscience Lab, ATR, Japan 

• A novel exoskeleton robot prototype is 

developed 

• Pneumatic muscles and electric motors 

are arranged in a optimal way to achieve 

weight-reduction and torque-controllability 

• Autonomous postural control using hybrid 

drive is presented 

• Experimental video is attached 


–178–


Symposium: Robots that Can See I 

Chair José Neira, Univ. de Zaragoza 

Co-Chair Dieter Fox, Univ. of Washington 

08:00–08:15 ThAT6.1* 

Semi-Plenary Invited Talk: Vision at Nanoscales 

Yu Sun, University of Toronto 

08:30–08:45 ThAT6.3 

Visual Place Categorization in Maps 

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08:15–08:30 ThAT6.2 

Semi-Plenary Invited Talk: Vision at Nanoscales 

Yu Sun, University of Toronto 

08:45–08:50 ThAT6.4 

CD SLAM - Continuous Localization and 

Mapping in a Dynamic World 

Katrin Pirker and Matthias Rüther and Horst Bischof 

Institute for Computer Graphics and Vision, Graz University of Technology, 

Austria 

• we perfrom large-scale, 

continuous, monocular localization 

and mapping 

• we handle short- and long-term 

scene dynamics by adding visibility 

information to each map point 

• efficient keyframe organization 

facilitates sliding window bundle 

adjustment and speeds up loop 

closure correction 

• scalability and localization 

accuracy is measured in a longterm 

experiment in a mixed 

indoor/outdoor scenery 


–179–


Symposium: Robots that Can See I 



08:50–08:55 ThAT6.5 

Vision-based Mobile Robot’s SLAM and 

Navigation in Crowded Environments 

Hiroshi Morioka, Sangkyu Yi 

Department of Computational Intelligence and System Science, 


Osamu Hasegawa 

Imaging Science and Engineering Laboratory, 


• We propose vision-based SLAM and 

navigation method that is effective even in 

crowded environments. 

• Proposed method extracts robust 3D 

feature points from sequential vision 

images and odometry. 

• We can eliminate unstable feature points 

extracted from dynamic objects. 

• We present experiments showing the 

utility of our approach in crowded 

environments. 

09:00–09:15 ThAT6.7 

Real-Time Photo-Realistic 3D Mapping 

for Micro Aerial Vehicles 

Lionel Heng, Gim Hee Lee, 

Friedrich Fraundorfer, and Marc Pollefeys 

Computer Vision and Geometry Lab, ETH Zürich, Switzerland 

• A stereo or Kinect camera generates 

a point cloud; a 3D occupancy grid is 

built. 

• We use IMU measurements to 

establish a 1-point RANSAC for 

visual odometry (VO) estimates. 

• The occupancy grid, images, and VO 

estimates are transmitted wirelessly 

to a ground control station (GCS). 

• The GCS incrementally constructs a 

3D textured map via a viewdependent 

projective texturing 

method. 

3D Textured Map from Kinect data 

08:55–09:00 ThAT6.6 

Vision Based Attitude And Altitude Estimation 

For UAVs In Dark Environments 

Ashutosh Natraj 

MIS Laboratory, University of Picardie Jules Verne, France 

Cédric Demonceaux 

Le2I Laboratory, University of Burgundy, France 

Pascal Vasseur 

LITIS Laboratory, University of Rouen, France 

Peter Sturm 

INRIA Grenoble – Rhônes-Alpes, France 

• A new sensor based on a laser projector 

and a fish-eye camera is proposed for 

UAV applications. 

• Attitude and altitude can be estimated in 

dark environment with good accuracy and 

in real-time. 

• The laser projects a circle on the ground 

then captured by the fish-eye camera. 

• The paper demonstrates the geometric 

relations between ground, the laser circle 

and the camera and proposes a simple 

and fast algorithm for parameter 

estimation 

09:15–09:30 ThAT6.8 

Stereo Visual Odometry for Pipe Mapping 

Peter Hansen 1 , Hatem Alismail 2 , Brett Browning 1,2 

and Peter Rander 2 

1 QRI8 Lab, Carnegie Mellon University, Qatar 

2 Robotics Institute/NREC, Carnegie Mellon University, USA 

• Generating appearance maps for 

inspection of Liquified Natural Gas pipes. 

• Using imagery from a customized stereo 

camera equipped on a pipe crawling robot. 

• Camera pose and scene point estimates 

obtained using Perspective-n-Points (PnP) 

and Sparse Bundle Adjustment (SBA). 

• Accurate pose estimates with error less 

than 1% for distance traveled. 


–180– 

Our stereo camera system, 

and an appearance map of the 

internal surface of a carbon 

steel pipe.


Mechanisms: Joint Design 

Chair Irene Sardellitti, Italian Inst. of Tech. 

Co-Chair Jean-François Brethe, LE HAVRE Univ. 

08:00–08:15 ThAT7.1 

Robust Estimation of Variable Stiffness 

in Flexible Joints 

Fabrizio Flacco and Alessandro De Luca 

Dipartimento di Informatica e Sistemistica, 

Università di Roma “La Sapienza”, Rome, Italy 

Irene Sardellitti and Nikos G. Tsagarakis 

Advanced Robotics Lab, Italian Institute of Technology, Genova, Italy 

• Stiffness is estimated using only position 

measurements on the motor sides and 

motor dynamic parameters 

• Residual based method to estimate the 

joint flexibility torque 

• Modified kinematic Kalman filter to handle 

discretization and quantization errors 

• Enhanced recursive least squares that 

does not suffer from lack of persistent 

excitation 

• Simulations and Experiments with the IIT 

AwAS joint 

08:30–08:45 ThAT7.3 

A bio-inspired condylar hinge joint 

for mobile robots 

Appolinaire C. Etoundi, Stuart C. Burgess 

Mechanical Engineering, University of Bristol, UK 

Ravi Vaidyanathan 

Mechanical Engineering, Imperial College London, UK 

• A bio-inspired design of hinge 

joint for mobile robots based on 

the human knee 

• Key feature of the design is its 

simplicity and similarity to the 

mammalian knee joint 

• Prototype model mimics the 

curved profiles of the human 

knee joint in order to achieve 

the benefits of high conformity 

and high stiffness and strength 

Femur 

Bush assembly 

Cap 

Nylon cable 

Strut x2 

Tibia 

Prototype coldylar joint 

08:15–08:30 ThAT7.2 

Cartesian stiffness matrix of manipulators with 

passive joints: analytical approach 

Anatol Pashkevich 1,2 , Alexandr Klimchik 1,2 , 

Stephane Caro 2 and Damien Chablat 2 

1 Ecole des Mines de Nantes, France 

2 Institut de Recherches en Communications et en Cybernetique de Nantes 

• Analytical expressions for stiffness matrix 

computation of serial kinematic chain with 

passive joints (stiffness mapping for 

manipulators with passive joints) 

• Recursive procedure for sequentially 

modification of the original stiffness matrix 

in accordance with the geometry of each 

passive joints. 

• Aggregation of the serial chain stiffness 

matrices with respect to any given 

reference point of the mobile platform 

• Ability to produce both singular and nonsingular 

stiffness matrices for serial and 

parallel manipulators 


–181– 

Virtual Joint Models of kinematic 

chain and parallel manipulator 

08:45–08:50 ThAT7.4 

The Mechanism of the Linear Load-Sensitive 

Continuously Variable Transmission 

with the Spherical Driving Unit 

Kenjiro TADAKUMA1 , Riichiro TADAKUMA2 (Yamagata University), 

Kazuki TERADA3 , Aiguo MING3 , Makoto SHIMOJO3 , 

Mitsuru Higashimori1 , Makoto Kaneko1 , 

1. Osaka University, 2. Yamagata University, 3. UEC, Japan 

• The proposed CVT mechanism consists of 

spherical drive, drive axis, motor housing, 

fixed bracket and linear sliding plate. 

• The mechanism changes the reduction ratio 

continuously by inclination angle of active 

rotational axis. 

• This linear mechanism has a load-sensitive 

function by changing inclination of active 

rotational axis in response to the load. 

• The prototype mechanical model linear loadsensitive 

continuously variable 

transmission has been developed and the 

effectiveness of the proposed mechanism 

has been confirmed by the experiments. 


use Arial Narrow 20pt


Mechanisms: Joint Design 

Chair Irene Sardellitti, Italian Inst. of Tech. 

Co-Chair Jean-François Brethe, LE HAVRE Univ. 

08:50–08:55 ThAT7.5 

Dynamic model of a Hyper-redundant Octopuslike 

Manipulator for Underwater Applications 

Rongjie Kang, Emanuele Guglielmino, David T. Branson 

and Darwin G. Caldwell 

Advanced Robotics Department, Italian Institute of Technology, Italy 

Asimina Kazakidi, Dimitris P. Tsakiris and John A. Ekaterinaris 

Foundation for Research and Technology – Hellas (FORTH), Greece 

• Octopus arm anatomy and morphology 

• Kinematics of an octopus arm inspired 

manipulator 

• Dynamic modeling for a single segment 

with parallel mechanism 

• Multiple-segment model 

• External forces due to the aquatic 

environment, such as buoyancy and 

hydrodynamic forces 

09:00–09:15 ThAT7.7 

Granular Stochastic Modeling of 

Robot Micrometric Precision 

Brethé Jean-François 

GREAH, Le Havre University, France 

• Modeling and quantifying robot precision when information from 

external sensors in the operating area is available 

• Pros and cons of usual precision criteria, especially repeatability 

and accuracy in this micrometric context 

• Computing the worst error position in the micrometric range 

• Granular stochastic modeling means holes and aggregates in the 

micrometric structure ! 

• Comparison to other precision modeling (input and output error 

modeling, interval analysis) 

08:55–09:00 ThAT7.6 

Three Module Lumped Element model of a 

Continuum Arm Section 

Nivedhitha Giri and Ian D. Walker 

Department of Electrical and Computer Engineering, Clemson University, 

United States of America 

• A continuum arm section is modeled using 

lumped model elements (mass, springs 

and dampers) 

• The model parameters are chosen to 

match the physical model of Octarm VI 

• Principles of Lagrangian Dynamics are 

used to derive the generalized forces in 

the system 

• Numerical results are compared with 

measurements 

• The linear approximation model provides 

sufficient information about the section’s 

configuration with a slight tradeoff in 

accuracy 


–182– 

Linearized Dynamic model


Autonomous Vehicles 

Chair Karl Iagnemma, MIT 

Co-Chair William Smart, Washington Univ. in St. Louis 

08:00–08:15 ThAT8.1 

Avoiding steering actuator saturation in off-road mobile 

robot path tracking via predictive velocity control 

Oliver Hach, Roland Lenain 

Cemagref, France 

Benoit Thuilot, Philippe Martinet 

Ifma and Lasmea, Université Blaise Pascal, France 

• High accurate control for off-road mobile 

robots 

• Observer to estimate grip conditions 

• Predictive control to ensure tracking 

accuracy and limit velocity in order to 

preserve path achievability 

• Algorithm applied to avoid actuator 

saturation: the maximal admissible 

velocity is computed to limit the steering 

angle to its saturation 

Views of the experimental offroad 

mobile robot 

08:30–08:45 ThAT8.3 

Integrating Stereo Structure 

for Omnidirectional Trail Following 

Christopher Rasmussen, Yan Lu, and Mehmet Kocamaz 

Dept. Computer & Information Sciences, University of Delaware, USA 

• A system for autonomous hiking/mountain-biking trail following is 

discussed 

• Structural information derived from dense stereo is integrated with color 

appearance information to segment and track the trail 

• The utility of structural information is demonstrated on ground truth 

segmentations, and live run results are presented 

Height snakes overlaid on height map for trail hypothesis 

08:15–08:30 ThAT8.2 

Consistent Pile-Shape Quantification for 

Autonomous Wheel Loaders 

Martin Magnusson and Håkan Almqvist 

AASS, Örebro University, Sweden 

• Experimental comparison of 

several algorithms for quantifying 

pile shapes. 

• Evaluate consistency w.r.t. 

viewpoint and sensor 

configuration. 

• Extensions of previous algorithms. 

• Novel method for segmenting pile 

from background in point-cloud 

data. 

• Propose to use quadric fitting for 

estimating relevant pile quantities. 


–183– 

Application shown above, 

variance of convexity measures 

illustrated below. 

08:45–08:50 ThAT8.4 

Performance Analysis and Odometry 

Improvement of an Omnidirectional Mobile 

Robot for Outdoor Terrain 

Genya Ishigami *1 , Elvine Pineda *2 , Jim Overholt *3 , 

Greg Hudas *3 , and Karl Iagnemma *2 

*1 Japan Aerospace Exploration Agency, JAPAN 

*2 Massachusetts Institute of Technology, USA 

*3 U.S. Army TARDEC, USA 

• An omnidirectional mobile robot that 

possesses high mobility in rough 

terrain is presented. 

• The robot has four active split offset 

caster (ASOC) modules, enabling the 

omnidirectional mobility. 

• The agility of the robot is evaluated in 

various configurations. 

• An odometry method along with 

kinematic constraints of the robot 

configuration is proposed. 

ASOC-driven Omnidirectional 

Mobile Robot


Autonomous Vehicles 

Chair Karl Iagnemma, MIT 

Co-Chair William Smart, Washington Univ. in St. Louis 

08:50–08:55 ThAT8.5 

Inertial Rotation Center Position Estimation for a 

Perching Treaded Vehicle 

Christopher Schmidt-Wetekam, Nicholas Morozovsky, 

and Thomas Bewley 

Dept. of Mechanical Engineering, UC San Diego, USA 

• A treaded vehicle, “Switchblade”, uses a 

combination of tread and body actuation in 

order to balance while in a perched 

configuration. 

• Identify stair edges without tactile or visual 

feedback by monitoring the position of the 

center of rotation 

• Use offset acceleration measurements to 

calculate rotation center position (RCP) 

• Variance of calculated RCP is inversely 

proportional to angular acceleration 

• RCP variance is minimized when 

accelerometers are equidistant and 

widely-spaced about the actual RCP 

The treaded rover, “Switchblade”, 

is intended to climb stairs using a 

perching manuever. 

09:00–09:15 ThAT8.7 

An Automated Truck Platoon for Energy Saving 

Sadayuki Tsugawa 

Department of Information Engineering, Meijo University, Japan 

Shin Kato 

Institute of Intelligent Systems, AIST, Japan 

Keiji Aoki 

Department of ITS Research, JARI, Japan 

• Objectives: energy saving and CO2 emission reduction for road transportation 

in addition to safety 

• Platoon: 3 heavy (25 ton) trucks 

• Functions: fully automated driving of an 

autonomous type 

• Lateral control: lane marker detection with computer vision 

• Longitudinal control: gap measurement with radar and lidar, V2V 

communications 

• Experiments: driving at 80 km/h with 10 m gap on a test track (oval, 3.2 

km) and along an expressway (8 km) 

• Fuel consumption improvement: 14 % (measurement) 

•CO2 emission reduction: 2.1 % (40 % penetration, simulation) 

08:55–09:00 ThAT8.6 

Improving near-to-near lateral control of 

platoons without communication 

Jano Yazbeck, Alexis Scheuer, 

Olivier Simonin and François Charpillet 

LORIA / INRIA, Nancy, France 

• Proposition of a decentralized local 

approach for a secure platooning. 

• Generic method based on memorization 

of the predecessor's path to reduce 

drastically lateral deviation. 

• Use of a longitudinal controller to ensure 


• Experimentations on differential wheeled 

Khepera III robots. 

09:15–09:30 ThAT8.8 

Context-Aware Video Compression 

for Mobile Robots 

Daniel A. Lazewatsky*, Bogumil Giertler**, Martha Witick**, Leah 

Perlmutter**, Bruce A. Maxwell**, and William D. Smart* 

*Dept of Computer Science & Engineering Washington University in St. Louis, USA 

**Dept of Computer Science Colby College, USA 

• Remote teleoperation 

often relies heavily on 

live video which travels 

over network(s) with 

unknown conditions 

• General-purpose video 

compression does not 

take into account 

conditions specific to 

robot operation 

• Integrating robot 

odometry into video 

compression pipeline can simultaneously reduce bandwidth and latency 

without affecting the robot operator’s task performance 


–184–


Towards Anthropomimetic Robots 

Chair Michael Beetz, Tech. Univ. München 

Co-Chair Frank Kirchner, Univ. of Bremen 

08:00–08:15 ThAT9.1 

A model of reference trajectory adaptation 

for interaction with objects 

of arbitrary shape and impedance 

Chenguang Yang and Etienne Burdet 

Imperial College London, UK 

• in the presence of an obstacle, trajectory 

drifts trial after trial 

• this tends to limit the interaction force 

• we have derived the dynamics of this 

human adaptation strategy and simulated it 

• this yields a stable trajectory adaptation 

behavior for robots 

reference 

trajectory 

1 

1 

5 

actual 

trajectory 

5 

9 9 

stiff object 

08:30–08:45 ThAT9.3 

Predictive Compliance for Interaction Control of 

Robot Manipulators 

José de Gea Fernández 1 and Frank Kirchner 1,2 

1 German Research Center for Artificial Intelligence (DFKI) 

Robotics Innovation Center 

2 Robotics Group, University of Bremen 

Bremen, Germany 

• Predictive Context-Based Adaptive 

Compliance (PCAC) architecture is 

presented 

• Context-based predictions for the 

selection and on-line modification of the 

compliance of a robot manipulator 

• A Bayesian predictor in the form of a 

Relevance Vector Machine combines 

the use of prior knowledge and 

expected sensory feedback to correct 

for an erroneous compliance in the 

case of a falsely-predicted context 

Robot performing the lifting 

experiment which makes use of 

context-based predictions to 

adjust the arm compliance 

08:15–08:30 ThAT9.2 

Generic Dynamic Motion Generation 

with Multiple Unilateral Constraints 

L. Saab O. Ramos N. Mansard and P. Souères 

LAAS, Université de Toulouse (UPS), France 

J.Y. Fourquet 

LGP, ENIT, France 

• Dynamic whole-body motion generation 

• Generic formulation of complex 

movements as stack of tasks including 

rigid contacts and unilateral constraints 

• Global resolution method involving a 

cascade of quadratic programming 

• Simulation based on the complete 

dynamic model of the humanoid robot 

HRP-2 


–185– 

A sitting task for HRP-2 involving 

several non-coplanar contacts 

08:45–08:50 ThAT9.4 

Parameterizing Actions to have the 

Appropriate Effects 

Lorenz Mösenlechner and Michael Beetz 

Intelligent Autonomous Systems Group 

Technische Universität München, Germany 

• Performing complex tasks in human 

environments is hard and requires 

reasoning (qualitative and quantitative) ative) 

• Plans use symbolic descriptions of f 

locations, objects, … which need to o be 

resolved at run-time 

• We present a fast and light-weight 

reasoning system that integrates 

Prolog, a physics engine and OpenGL nGL 

to infer locations under the constraints aints 

of the current task


Towards Anthropomimetic Robots 

Chair Michael Beetz, Tech. Univ. München 

Co-Chair Frank Kirchner, Univ. of Bremen 

08:50–08:55 ThAT9.5 

Physics-based Modeling of an 

Anthropomimetic Robot 

Steffen Wittmeier, Michael Jäntsch, 

Konstantinos Dalamagkidis and Alois Knoll 

Faculty of Informatics, TU München, Germany 

• Control of highly-redundant, tendon-driven 

robots remains a very challenging task. 

• Here, the reverse-engineered derivation of f 

a physics-based model of a hand-crafted, 

anthropomimetic robot arm is presented. 

• The skeleton model was derived from a 

total of 25 laser-scans and actuator as 

well as sensor models were implemented. 

• Moreover, a muscle control algorithm was 

implemented to demonstrate and validate 

the dynamics of the simulation model. 

• The developed model will subsequently be e 

employed as an internal dynamics model 

for robot control. 

Prototype P of fh the anthropomimetic 

h i i 

robot ECCE-I. 

09:00–09:15 ThAT9.7 

A comparison between joint level torque 

sensing and proximal F/T sensor torque 

estimation: implementation on the iCub 

M. Randazzo, M. Fumagalli, F. Nori, L. Natale, 

G. Metta, G.Sandini 

Robotics Brain and Cognitive Science Department, 

Instituto Italiano di Tecnología, Italy 

• Two different approaches of 

achieving compliant control on a 

humanoid robotic arm are compared. 

• A model-based approach is used to 

estimate both the internal and the 

external wrenches using a single 

proximal six-axis force-torque sensor. 

• On the other hand, integrated torque 

sensors have been developed to 

directly measure the joint torques. 

• The obtained estimations and 

torques measurements are 

experimentally compared and the 

benefits / disadvantages of the two 

approaches are discussed. 

08:55–09:00 ThAT9.6 

Reexamining Lucas-Kanade Method for Real-Time 

Independent Motion Detection: An Application to 

the iCub Humanoid Robot 

C. Ciliberto, U. Pattacini, L. Natale, F. Nori and G. Metta 

RBCS, Istituto Italiano di Tecnologia, Italy 

• The motionCUT algorithm is presented to 

perform real-time independent motion 

detection for applications in robotics. 

• The canonical Lucas-Kanade method for 

optical flow is analyzed to derive a criterion 

that is apt to discriminate between the 

robot egomotion and independent motion 

in camera-acquired image streams. 

• Extensive experimental validation is 

carried out on the iCub humanoid robot, 

demonstrating that the proposed system 

responds smoothly to changes in the 

environmental conditions. 

• A task involving the stereo gaze tracking of 

a moving target is examined as a practical 

example of the motionCUT capabilities 


–186– 

Stereo tracking of a person walking in 

a cluttered environment 

09:15–09:30 ThAT9.8 

Computation with mechanically coupled springs 

for compliant robots 

Hidenobu Sumioka, Helmut Hauser, 

and Rolf Pfeifer 

the Department of Informatics 

University of Zurich, Switzerland 

• We identify the computation that a 

compliant physical body can achieve. 

• Actuation of the springs around a joint is 

used as a computational device. 

• Only a linear and static readout is adapted 

to compute the output. 

• Computer simulation shows the spring 

network can emulate nonlinear 

combinations which need temporal 

integration. 

An overview of information 

processing in the system and the 

emulated trajectories .The system 

can emulate 2 nd order systems and 

10 th ones accurately.

Session ThBT1 Continental Parlor 1 Thursday, September 29, 2011, 10:00–11:30 

Control for Manipulation & Grasping 

Chair Jing Xiao, UNC-Charlotte 

Co-Chair Luigi Villani, Univ. di Napoli Federico II 

10:00–10:15 ThBT1.1 

Adaptive Sliding Mode Control of Grasped 

Object Slip for Prosthetic Hands 

Erik D. Engeberg 

Mechanical Engineering Department, The University of Akron, USA 

Sanford Meek 

Mechanical Engineering Department, The University of Utah, USA 

• The first robustly stable grasped object 

slip prevention algorithm for a prosthetic 

hand. 

• The adaptive integral sliding mode slip 

prevention controller also minimizes 

inadvertent object crushing when slip 

occurs 

• Slip is detected by band pass filtering the 

shear force derivative signal to amplify 

vibrations that occur 

• Five different values of object stiffness are 

tested with three different control 

algorithms 

10:30–10:45 ThBT1.3 

Self-tuning Cooperative Control of Manipulators 

with Position/Orientation Uncertainties in The 

Closed-Kinematic Loop 

Farhad Aghili 

Canadian Space Agency (CSA), Canada 

• Adaptive control of cooperative 

manipulators in the presence of 

uncertainties in the closed-loop 

kinematic loop 

• The kinematic parameters are 

estimated in real-time using two 

cascaded estimators 

• No force measurement is required to 

deal with kinematic uncertainties of 

interconnected robotic system. 

• Convergence and stability of the 

adaptive control process can be 

ensured if the direction of angular 

velocity of the object does not remain 

constant over time. 

• Experimental results are presented 

The cooperative dual-arm 

manipulator system 

10:15–10:30 ThBT1.2 

Cartesian Impedance Control For A Variable 

Stiffness Robot Arm 

Florian Petit and Alin Albu-Schäffer 

Institute of Robotics and Mechatronics, German Aerospace Center (DLR), 

Germany 

• Combination of an active Cartesian 

impedance controller and the variable 

stiffness robot. 

• An algorithm to optimize the passive and 

active Cartesian stiffness for increased 

tracking performance is presented. 

• The extended stiffness range provided by 

the algorithm is shown. 

• Implementation and experimental results 

on the DLR Hand Arm System are given. 


–187– 

The DLR Hand Arm System 

10:45–10:50 ThBT1.4 

Kinematic Control with Force Feedback for a 

Redundant Bimanual Manipulation System 

F. Caccavale*, V. Lippiello + , G. Muscio*, F. Pierri*, F. Ruggiero + , L. Villani + 

*Dipartimento di Ingegneria e Fisica dell’Ambiente, Università della Basilicata, Italy 

+ Dipartimento di Informatica e Sistemistica, Università di Napoli Federico II, Italy 

• Kinematic model for a multi-fingered 

redundant bimanual manipulation system 

with elastic contacts, while grasping an 

object 

• Two-stages control scheme 

• First stage: inverse kinematics with 

redundancy resolution to compute joint 

references 

• Second stage: parallel force/position 

control to impose desired object 

motion and normal contact forces 

• Secondary tasks accomplished through a 

prioritized task-sequencing strategy


Control for Manipulation & Grasping 

Chair Jing Xiao, UNC-Charlotte 

Co-Chair Luigi Villani, Univ. di Napoli Federico II 

10:50–10:55 ThBT1.5 

Robust Manipulation 

for Temporary Lack of Sensory Information 

by a Multi-Fingered Hand-Arm System 

Akihiro Kawamura, Kenji Tahara, Ryo Kurazume 

and Tsutomu Hasegawa 


• A new manipulation scheme against the 

temporary lack of sensory information is 

proposed. 

• According to the availability of sensory 

information, the controlled target frame of 

the object is switched from the real one to 

the virtual one, and vice versa. 

• A new virtual object frame based on an 

attitude of each fingertip is proposed. 

• A numerical simulation result shows that 

the proposed method realizes stable 

object manipulation even if sensory 

information is unavailable. 

Multi-fingered hand-arm system 

11:00–11:15 ThBT1.7 

Impedance control of a non-linearly coupled 

tendon driven thumb 

Maxime Chalon, Werner Friedl, Jens Reinecke, Thomas 

Wimboeck and Alin Albu-Schaeffer 

Institute of Robotics and Mechatronics, German Aerospace Center 

• The thumb nonlinear actuation structure 

provides improved torque and range of 

motion but creates new control challenges 

• A singular perturbation based controller is 

designed and implemented on the real 

system 

• Experiments and simulation are performed 

to verify the performances 

• Due to the absence of link side sensor, a 

projected gradient method is implemented 

in real-time to estimate the joint position 

10:55–11:00 ThBT1.6 

Determining “Grasping” Configurations for a 

Spatial Continuum Manipulator 

Jinglin Li and Jing Xiao 

IMI Lab, Department of Computer Science 

University of North Carolina - Charlotte, USA 

• A continuum manipulator, such as a multisection 

trunk/tentacle robot, is promising 

for deft manipulation of a wide range of 

objects of different shapes and sizes. 

• This paper presents a general and 

complete analysis to determine grasping 

configurations for any given 3D object by 

a spatial continuum manipulator with three 

constant-curvature sections; the 

curvature, length, and orientation of each 

section are continuous variables. 

• Our method finds all types of grasping 

configurations through solving intersection 

constraint equations. The 

implementation results show its efficiency 

for on-line operation. 


–188– 

One grasping configuration, 

where sections 3 and 2 of the 

continuum manipulator wrap 

around the object. 

11:15–11:30 ThBT1.8 

Energy Shaping Control of Robot Manipulators 

in Explicit Force Regulation Tasks with Elastic 

Environments 

David Navarro-Alarcon, Peng Li, and Hiu Man Yip 

Departament Mechanical and Automation Engineering, 

The Chinese University of Hong Kong, Hong Kong S.A.R. 

• Physical interaction of a robot manipulator 

with a purely elastic environment 

• The closed-loop elastic energy is shaped 

based on the available force sensory 

feedback 

• Exact force regulation is achieved by 

means of a force-modulated “energy 

injection” to the system 

• Simple experimental results are presented 

to validate the approach 

Conceptual representation


Mapping 

Chair Cyrill Stachniss, Univ. of Freiburg 

Co-Chair Mitch Bryson, Univ. of Sydney 

10:00–10:15 ThBT2.1 

Visual Mapping with Uncertainty 

for Correspondence-free Localization 

using Gaussian Process Regression 

T. Schairer1 , B. Huhle1 , P. Vorst2 , A. Schilling1 and W. Straßer1 1Dept. of Graphical Interactive Systems, University of Tübingen, Germany 

2Dept. of Cognitive Systems, University of Tübingen, Germany 

• Setting: 

• Appearance-based localization using 

low-resolution omnidirectional images 

• Gaussian Process regression for 

modeling image variation of the scene 

• Problem: 

• Time-consuming acquisition of reference 

images with exact position information 

• Our solution: 

• Use available odometry data 

• Learn a probabilistic map by fusing 

uncertain training data 

• Quality increases with every new run 

Reference images (dots), odometry 

(dotted), ground-truth (solid) and 

our approach (dashed). 

10:30–10:45 ThBT2.3 

Dense visual mapping of large scale 

environments for real-time localisation 

Maxime Meilland and Patrick Rives 

INRIA Sophia Antipolis Méditerrannée, France 

Andrew Ian Comport 

CNRS, I3S Laboratory, Université Nice Sophia Antipolis, France 

• Custom sensor for augmented visual 

sphere construction with a unique 

center of projection. 

• Calibration of a ring of divergent stereo 

cameras. 

• Automatic environment mapping using 

an ego-centered representation. 

• Accurate real-time robot localisation 

navigating locally within the learnt 

model. 

10:15–10:30 ThBT2.2 

Distributed Large Scale Terrain Mapping for Mining 

and Autonomous Systems 

Paul Thompson, Eric Nettleton and Hugh Durrant-Whyte 

The Australian Centre for Field Robotics (ACFR) 

The University of Sydney, Australia 

• A method for efficient fusion, estimation and distribution of large 

scale terrain 

• Based on sparse information smoothing, related to finite-elements 

(FEM) and Gaussian processes (GPs) 

• Results for efficient fusion and distribution 

• Applied to multiple terrain scanning lasers in mining 

10:45–10:50 ThBT2.4 

Hierarchies of Octrees for Efficient 3D Mapping 

K.M. Wurm1 D. Hennes2 D. Holz3 R.B. Rusu4 C. Stachniss1 K. Konolige4 W. Burgard1 1 Dep. of Computer Science, University of Freiburg, Germany 

2 Dep. of Knowledge Engineering, Maastricht University, The Netherlands 

3 Autonomous Intelligent Systems Group, University of Bonn, Germany 

4 Willow Garage Inc., CA, USA 

• Multi-resolution 3D representation 

organized as a tree of submaps 

• Each submap is updated and 

transformed individually 

• Models occupied and free space 

efficiently 

• Application to table-top 

manipulation and autonomous 

exploration 


–189– 

Top: model of a table top scene. 

Bottom: illustration of the map hierarchy


Mapping 

Chair Cyrill Stachniss, Univ. of Freiburg 

Co-Chair Mitch Bryson, Univ. of Sydney 

10:50–10:55 ThBT2.5 

A Comparison of Feature and Pose-Based 

Mapping using Vision, Inertial and GPS on a UAV 

Mitch Bryson and Salah Sukkarieh 


• Presents and compares two different 

approaches to integrating IMU, GPS 

and monocular vision camera data 

from an Unmanned Aerial Vehicle for 

building large-scale 3D terrain 

reconstructions. 

• Novel pose-only non-linear leastsquares 

formulation that optimises 

relative camera poses based on 1D 

epipolar constraints imposed by 

vision matches. 

• Results presented within an 

environmental mapping project over 

outback Australia 

11:00–11:15 ThBT2.7 

Occupancy Grid Rasterization in Large 

Environments for Teams of Robots 

Johannes Strom Edwin Olson 

Compute Science and Engineering , University of Michigan, USA 

• We present an efficient algorithm for 

dynamic occupancy grid computation in 

large environments (220m x 170m and up) 

• Map update cost is proportion to impact of 

any loop closures 

• Rasterization can be sped up by 

identifying redundant sensor data 

• Applicable in dynamic environments by 

not rasterizing transient objects 

Rasterized map using proposed 

method (100m by 160m) 

10:55–11:00 ThBT2.6 

Autonomous Semantic Mapping for Robots 

Performing Everyday Manipulation Tasks 

in Kitchen Environments 

Nico Blodow, Lucian Cosmin Goron, Zoltan-Csaba Marton, Dejan 

Pangercic, Thomas Ruehr, Moritz Tenorth, and Michael Beetz 

Intelligent Autonomous Systems, Technische Universitaet Muenchen, Germany 

� Autonomous exploration including the selection of the next best view 

pose in order to actively explore unknown space based using a visibility 

kernel and associated costmaps 

� Registration, segmentation, and interpretation of color point cloud data 

from low-cost devices 

• Segmentation of differences of point clouds through interaction using the 

robot manipulator 

11:15–11:30 ThBT2.8 

Autonomous mapping for inspection 

of 3D structures 

Mahmoud Tavakoli, Ricardo Faria, 

Lino Maques, Anibal T. de Almeida 

Institute for Systems and Robotics, University of Coimbra, Portugal 

• Multiple terrain robots map a 

structure which should be explored 

by a pole climbing robot 

• Roombas are equipped with a 

wide angle camera 

• Patterns are fixed on the climbing 

robot 

• ARToolkit library is used for pose 

estimation between each camera 

and each pattern 

• A leap-frog methodology is applied 

for the cooperative localization of 

the robots 


–190–


Randomized Planning & Kinematic Control 

Chair Nancy Amato, Texas A&M Univ. 

Co-Chair Elie Shammas, American Univ. of Beirut 

10:00–10:15 ThBT3.1 

FIRM: Feedback Controller-Based 

Information-state RoadMap 

A framework for planning under uncertainty 

Ali-akbar Agha-mohammadi, Suman Chakravorty, and Nancy Amato 

Computer Science and Engineering Dept., Texas A&M University, USA 

Aerospace Engineering Dept., Texas A&M University, USA 

• Generalizing PRM to belief space 

preserving the edge independence 

and thus breaking the curse of history 

in POMDPs 

• Inducing reachable belief nodes 

• Incorporating obstacles in planning 

• Online planning and replanning 

capabilities 

• Providing a feedback law over belief 

space 

10:30–10:45 ThBT3.3 

Optimal Probabilistic Robot Path Planning with 

Missing Information 

Mohamad Ali Movafaghpour and Ellips Masehian 

Faculty of Engineering, Tarbiat Modares University, Iran 

• The missing information about the 

outcomes of a robot’s actions can be 

modeled with a Stochastic Markov 

Decision Process with Hidden Variables, 

the exact values of which are not known at 

the beginning of path planning. 

• A Stochastic Dynamic Programming 

solution approach for planning with 

missing information is formulated as a 

Linear Programming (LP) model. 

• A new algorithm called Incremental 

Assumptive Probabilistic Planning (IAPP) 

is proposed for incrementally incorporating 

active Hidden Variables into the beliefstate 

space and solving an LP. 

• An optimality proof for the path generated 

by the IAPP and a method for treating 

large-scale LP models are also presented. 

A4 B4 

A6 

B5 

B6 

C1 

C2 

C3 

C4 D4 

D1 E1 

F1 

F2 

F3 

E4 F4 

F5 

C6 D6 E6 F6 

The robot’s start and goal points 

are on A4 and F4, and the 

open/closed states of the cells B5 

and E4 are unknown at the 

beginning. These cells represent 

the Hidden Variables whose 

states affect the outcomes and 

costs of certain actions. 

10:15–10:30 ThBT3.2 

Computing Spanners of 

Asymptotically Optimal Probabilistic Roadmaps 

James D. Marble and Kostas E. Bekris 

University of Nevada, Reno, USA 

• Asymptotically optimal motion planning 

algorithms produce very dense 

roadmaps 

• Spanner graphs are sub-graphs with 

fewer edges and guarantees on 

resulting path length 

• This work combines these two 

concepts to produce an asymptotically 

near-optimal planner 

−Path degradation is bounded 

• Small sacrifice in path quality results in: 

• Large savings in graph density 

• Faster online query resolution 


–191– 

Given a dense roadmap (above) the 

resulting sparse spanner (below) 

preserves path quality. 

10:45–10:50 ThBT3.4 

Asymptotically-optimal Path Planning for 

Manipulation using Incremental Sampling-based 

Algorithms 

Alejandro Perez, Alexander Shkolnik, Seth Teller, Matthew R. Walter 

Computer Science and Artificial Intelligence Laboratory, MIT 

Sertac Karaman, Emilio Frazzoli 

Laboratory for Information and Decision Systems, MIT 

• Optimal motion planning for manipulation 

based on the RRT* 

• Efficient sampling using Ball Trees 

• Rapid generation of low-cost solutions 

• Dual-arm planning on Willow Garage’s 

PR2 robot 

RRT 

BT+RRT* 

Time-lapse images of RRT and 

BT+RRT* trajectories after 2,000 

iterations (12DOF)


Randomized Planning & Kinematic Control 

Chair Nancy Amato, Texas A&M Univ. 

Co-Chair Elie Shammas, American Univ. of Beirut 

10:50–10:55 ThBT3.5 

Quasi-Static Motion Planning on Uneven Terrain 

for a Wheeled Mobile Robot 

Vijay Eathakota , Aditya Gattupalli and K Madhava Krishna 

Robotics Research Center, IIIT Hyderabad, India 

• We develop a motion planning algorithm 

based on the RRT, connecting the starting 

and the end goal positions of the Wheeled 

Mobile Robot on Uneven Terrain while 

minimizing wheel slip 

• The curves connected the adjacent nodes of 

the RRT are developed using the forward 

motion problem based on Peshkin’s 

minimum energy principle 

• The result of this planner is a set of ordinary 

differential equations (ODEs) along with a set 

of Differential Algebraic Equations (DAEs) 

• Simulation Results prove the efficacy of the 

planner 

Wheeled Mobile Robot on 

Uneven Terrain 

11:00–11:15 ThBT3.7 

Exact Motion Planning Solution for Principally 

Kinematic Systems 

Elie Shammas 

Mechanical Engineering, American University of Beirut, Lebanon 

Mauricio de Oliveira 

Mechanical and Aerospace Eng’g, University of California San Diego, USA 

• Motion planning problem for principally 

kinematic systems is solve analytically. 

• The input of the motion planning problem 

is a planar curve and the output is the time 

evolution of the input shape variables. 

• The base or shape variables are solved 

for analytically so that the kinematic snake 

traverses exactly a planar trajectory. 

• The motion planning techniques are 

tested with Serpenpid, sinusoidal, and 

cubic planar curves. 

Kinematic snake traversing a 

cubic spline curve. 

10:55–11:00 ThBT3.6 

Minimum-time trajectories for kinematic mobile 

robots and other planar rigid bodies 

Andrei Furtuna, Wenyu Lu, 

Weifu Wang, Devin Balkcom 

Computer Science, Dartmouth College, USA 

• A generalized model of mobile robots includes Dubins, Reeds- 

Shepp and other vehicles as special cases. 

• Pontryagin’s principle gives strong necessary conditions on the 

minimum-time trajectories. 

• We present an algorithm that efficiently searches for minimum-time 

trajectories. 

A minimum-time trajectory for an 

omnidirectional vechicle 

11:15–11:30 ThBT3.8 

Continuous-Curvature Kinematic Control for 

Path Following Problems 

Vicent Girbés and Leopoldo Armesto and 

Josep Tornero and J. Ernesto Solanes 

Institute of Design and Manufacturing, Technical University of Valencia, Spain 

• Smooth kinematic controller is 

introduced, mathematically and 

algorithmically. 

• Kinematic and Dynamic constraints 

are taken into account. 

• Capability of combine the kinematic 

controller with global path planners 

or vision systems as a waypoints 

generators. 

• Vision-based industrial forklift is 

used as an AGV for testing the 

proposed method. 

• Benchmark evaluation is done to 

show better performance than 

Pure-Pursuit methods. 


–192– 

AGV with a vision-based smooth 

kinematic controller

Session ThBT4 Continental Ballroom 4 Thursday, September 29, 2011, 10:00–11:30 

Symposium: Stochasticity in Robotics and Biological Systems II 


Co-Chair Dejan Milutinovic, Baskin School of Engineering, UC Santa Cruz 

10:00–10:15 ThBT4.1 

A Trajectory-based Calibration Method for 

Stochastic Motion Models 

Ezequiel Di Mario, Gregory Mermoud, Massimo 

Mastrangeli and Alcherio Martinoli 

Distributed Intelligent Systems and Algorithms Laboratory, 

EPFL, Switzerland 

• Quantitative method based on the 

Correlated Random Walk (CRW) model 

• Minimize Kolmogorov-Smirnov distance 

between step length and step angle 

distributions 

• Validation on real trajectories of 

centimeter-sized water-floating robots 

• Sensitivity analysis on a multi-robot 

simulation: effect of parameter 

perturbation on the predicted 

distributions of self-assembled 

structures 

10:30–10:45 ThBT4.3 

Stochastic optimal control with variable 

impedance manipulators in presence of 

uncertainties and delayed feedback 

Bastien Berret, Serena Ivaldi, Francesco Nori and Giulio Sandini 

Robotics, Brain and Cognitive Sciences, Italian Institute of Technology, Italy 

• Recent findings suggest that cocontraction, 

or the human ability to change 

the intrinsic musculo-skeletal compliance, 

plays a crucial role when dealing with 

uncertainties and unpredictability 

• We use numerical methods for stochastic 

optimal control of movements with 

variable impedance manipulators in 

presence of uncertainties 

• We show that actively varying the system 

compliance can prevent the 

disadvantages of delayed feedback if 

coupled with a global and centralized 

feedforward motor plan which exploits 

muscle co-contraction to achieve 

(feedback free) disturbance rejection 

Example of two-joint arm moving 

in a divergent force field, using 

variable impedance control 

10:15–10:30 ThBT4.2 

Cooperative Multi-Agent Inference over 

Grid Structured Markov Random Fields 

Ryan Williams and Gaurav Sukhatme 

Departments of Electrical Engineering and Computer Science, University of 

Southern California, USA 

• Cooperative inference with distributed 

observations, uncertainty modeled by grid 

structured pairwise Markov Random Field 

(MRF) 

• Proposed Multi-Agent MRF (MAMRF) 

decomposes global inference into local 

belief exchanges and per-agent inference 

problems 

• Loopy belief propagation for approximate 

inference with synchronous and proposed 

region-of-influence message passing 

• Spatial mapping simulations over realworld 

Moderate Resolution Imaging 

Spectroradiometer (MODIS) dataset 

illustrates promising performance 


–193– 

Cooperative spatial mapping 

simulation over MODIS sampling 

10:45–10:50 ThBT4.4 

Stochastic optimization of a chain sliding mode 

controller for the mobile robot maneuvering 

A.V. Terekhov, J.-B. Mouret, C. Grand 

Institut des Systèmes Intelligents et de Robotique, UPMC-CNRS, France 

• Study of aggressive steering 

maneuvers 

• A chain sliding mode controller is 

proposed 

• Uses stochastic optimization for 

controller parameters tuning 

• Validation performed on the real robot


Symposium: Stochasticity in Robotics and Biological Systems II 


Co-Chair Dejan Milutinovic, Baskin School of Engineering, UC Santa Cruz 

10:50–10:55 ThBT4.5 

Programmable 3D Stochastic Fluidic Assembly 

of cm-scale Modules 

Michael T. Tolley 


Hod Lipson 

School of Mechanical and Aerospace Engineering, Cornell University, USA 

• Stochastic Fluidic Assembly (SFA) 

offers a potential approach to 

achieving programmable matter 

• We present a minimalistic 

component approach that has led to 

three experimental advances in cmscale 

SFA: 

• Robust assembly 

• Parallel hierarchical assembly 

• Fully automated assembly using 

pressure sensor feedback 

• This biologically inspired approach 

could lead to batch manufacturing of 

small scale target structures 

Parallel Stochastic Fluidic Assembly 

of Non-Planar 3D Structures 

11:00–11:15 ThBT4.7 

Visual Localization in Fused Image and Laser 

Range Data 

Nicholas Carlevaris-Bianco and Anush Mohan 

Dept. Electrical Eng. & Computer Science, University of Michigan, U.S.A. 

James R. McBride 

Research and Innovation Center, Ford Motor Company, U.S.A. 

Ryan M. Eustice 

Dept. Naval Architecture & Marine Eng., University of Michigan, U.S.A. 

• Using a richly instrumented 

robotic platform the algorithm 

builds a city scale map of 

sparse visual features from 

co-registered LIDAR and 

image data. 

• Localization is performed 

using the map and a low cost 

instrumented camera 

system. 

• Results presented on data 

collected over multiple years, 

showing strong robustness 

w.r.t. dynamic changes in the 

environment. 

10:55–11:00 ThBT4.6 

Hierarchical Congestion Control 

for Robotic Swarms 

Vinicius Graciano Santos and Luiz Chaimowicz 

VeRLab, UFMG, Brazil 

• One of the main challenges in swarm 

navigation is to avoid congestions 

• We propose the use of hierarchical 

abstractions in conjunction with traffic 

controls rules to solve this problem 

• Simulated and real experiments are 

presented to validate the approach. 

• Results show that our method allows the 

swarm to navigate without congestions in 

a smooth and coherent fashion 


–194– 

Avoiding congestions between 

groups of robots 

11:15–11:30 ThBT4.8 

Mathematical Analysis of the Minimum Variance 

Model of Human-Like Reaching Movements 

Mikhail Svinin and Motoji Yamamoto 

Mechanical Engineering Department 

Faculty of Engineering 


• Open-loop optimal trajectory planning in 

stochastic formulation (control signaldependent 

noise). 

• Performance index: cumulative average 

positional variance over the interval (T,T+R). 

• Limiting case of infinite R is considered for 

several classes of models (stable plant, n-th 

order integrator, diagonizable systems). 

• For n-th order integrator and diagonazible 

systems with zero eigenvalues, the limiting 

model is equivalent to the conventional 

minimum effort model 

• The conventional minimum jerk model is a 

partial case of the minimum variance model. 

Post-movement covariance


Symposium: Humanoid Technologies 

Chair Tamim Asfour, Karlsruhe Inst. of Tech. (KIT) 

Co-Chair Kazuhito Yokoi, National Inst. of AIST 

10:00–10:15 ThBT5.1* 

Semi-Plenary Invited Talk: Optimal control of motions 

of humanoid robots 

Katja Mombaur, University of Heidelberg 

10:30–10:45 ThBT5.3 

Hardware Improvement of Cybernetic Human 

HRP-4C 

for Entertainment Use 

Kenji Kaneko, Fumio Kanehiro, Mitsuharu Morisawa, 

Tokuo Tsuji, Kanako Miura, Shin’ichiro Nakaoka, 

Shuuji Kajita and Kazuhito Yokoi 

AIST, Japan 

• Hardware improvement of cybernetic 

human HRP-4C for entertainment 

• New hand redesigned to realize a humansize 

hand with realistic skin and the 

average figure of a young Japanese 

female 

• New foot with active toe joint for realizing 

human-like walking motion 

• New eye with camera towards both 

providing visual information for operators 

and realization of motion in response to 

spectators 

Cybernetic human HRP-4C 

improved with, 

new hands, an active toe joint, 

and a new eye with camera 

10:15–10:30 ThBT5.2 

Semi-Plenary Invited Talk: Optimal control of motions 

of humanoid robots 

Katja Mombaur, University of Heidelberg 

10:45–10:50 ThBT5.4 

Human Robot HRP-4 

Humanoid Robotics Platform with Lightweight and Slim Body 

Kenji Kaneko, Fumio Kanehiro, Mitsuharu Morisawa 

AIST, Japan 

Kazuhiko Akachi, Go Miyamori, 

Atsushi Hayashi, Noriyuki Kanehira 

Kawada Industries, Inc., Japan 

• A new lightweight and slim body with a 

height of 151 [cm] and weight 39 [kg] 

following the legacy of the HRP series 

• Total of 34 degrees of freedom, including 

7 degrees of freedom for each arm to 

facilitate object handling 

• Cost reduction by adoption of modularized 

units and common parts, reviewing the 

machining process, streamlining the basic 

specification, and so on 

• Improved research efficiency due to the 

adoption of OpenRTM-aist, which enables 

the use of domestic and international 

software assets 


–195– 

HRP-4 

( Humanoid robotics platform – 4 )


Symposium: Humanoid Technologies 

Chair Tamim Asfour, Karlsruhe Inst. of Tech. (KIT) 

Co-Chair Kazuhito Yokoi, National Inst. of AIST 

10:50–10:55 ThBT5.5 

Weakly collision-free paths for continuous 

humanoid footstep planning 

Nicolas Perrin (1) and Olivier Stasse (2) 

Florent Lamiraux (1) and Eiichi Yoshida (2) 

(1) CNRS/LAAS, Université de Toulouse UPS, INSA, INP, ISAE, France 

(2) CNRS-AIST JRL, UMI3218/CRT, Tsukuba, Japan 

• New notion of collisionfreeness 

• Equivalence between an 

example of discrete 

footstep planning and 

classical 2D continuous 

motion planning 

• Possibility to use PRM or 

RRT for footstep planning in 

a sound and complete way 

A continuous weakly collision-free path for the green 

shape is converted into a discrete sequence of steps 

11:00–11:15 ThBT5.7 

Bipedal walking control 

based on Capture Point dynamics 

Johannes Englsberger, Christian Ott, Maximo A. Roa, 

Alin Albu-Schäffer, and Gerhard Hirzinger 

Institute of Robotics & Mechatronics, German Aerospace Center (DLR) 

• Capture Point (CP) dynamics is an 

effective and simple tool to design 

feedback controllers for bipedal 

robots 

• CP control is very flexible and can 

be used for high-level control 

strategies like push recovery and 

online step adjustment 

• CP control allows to limit the ZMP 

to the support polygon explicitly 

• Robustness of CP feedback 

control is shown analytically, in 

simulation and in experiments on 

DLR Biped 

DLR Biped 

CP shifting with ZMP 

CP 

COM 

ZMP 

Shifting CP 

from foot to foot 

10:55–11:00 ThBT5.6 

Using a Multi-Objective Controller to Synthesize 

Simulated Humanoid Robot Motion with 

Changing Contact Configurations 

Karim Bouyarmane and Abderrahmane Kheddar 

CNRS-AIST JRL UMI3218/CRT, AIST, Japan 

LIRMM, Montpellier 2 University, France 

• Non-gaited acyclic multi-contact closedloop-based 

motion generation tool for a 

humanoid robot. 

• Input: sequence of static contact-transition 

postures, output: dynamically consistent 

motion between the postures. 

• Multi-objective control: CoM, end-effector, 

posture. Two kinds of objectives: set-point 

objective and target objective. 

• Objectives automatically decided by a 

finite-state machine. 

• Results in constraint-based dynamic 

simulation (non real-time). 

11:15–11:30 ThBT5.8 

Human-like Walking 

with Toe Supporting for Humanoids 

Kanako Miura, Mitsuharu Morisawa, Fumio Kanehiro 

Shuuji Kajita, Kenji Kaneko and Kazuhito Yokoi 

Intelligent Systems Research Institute, 

National Institute of Advanced Industrial Science and Technology, Japan 

• Three characteristics of human walking 

were added: single toe support, stretching 

knees, and foot swing motion. 

• Modifications to add these characteristics 

are made based on the conventional 

walking pattern generator. 

• The generated pattern is converted to a 

feasible pattern using a prioritized 

optimization technique. 

• A stabilizer is also improved to maintain 

ZMP inside of tiny supporting area of toe. 

• The aimed walking motion was 

successfully demonstrated with HRP-4C. 


–196–


Symposium: Robots that Can See II 



10:00–10:15 ThBT6.1* 

Semi-Plenary Invited Talk: Vision in the Field 

Peter Corke, QUT 

10:30–10:45 ThBT6.3 

Capturing City-level Scenes with a 

Synchronized Camera-Laser Fusion Sensor 

Yunsu Bok, Dong-Geol Choi, Yekeun Jeong, and In So Kweon 

RCV Lab., EE Dept., KAIST, Korea 

• A novel camera-laser fusion system : 

synchronized to be mounted on a fastmoving 

ground vehicle 

• Reconstruction by accumulating laser 

scans : motion estimation without the 

2D assumption and refinement by a few 

closed loops 

• Moving object detection : 

a solution for detecting moving objects 

which cannot be detected by cameras 

10:15–10:30 ThBT6.2* 

Semi-Plenary Invited Talk: Vision in Space 

Andrew Howard, Space Exploration Technologies (SpaceX) 

10:45–10:50 ThBT6.4 

Vehicle Detection and Tracking at Nighttime for 

Urban Autonomous Driving 

Hossein Tehrani, Koji Takahashi, Seiichi Mita, 

Smart Vehicle Research Center, Toyota Technological Institute, Japan 

David McAllester 

Toyota Technological Institute at Chicago, USA 

• Present a weighted deformable object 

model for detecting and tracking vehicles 

using monocular infra-red camera. 

• Filters are learned through combination of 

latent support vector machine and 

histograms of oriented gradients. 

• Detected vehicles are tracked by 

calculating maximum HOG features 

compatibility for both root and parts. 

• Achieve an average vehicle detection and 

tracking rate of 83.78% with very low false 

positive. 


–197– 

Multi Vehicles detection and 

tracking at night time


Symposium: Robots that Can See II 



10:50–10:55 ThBT6.5 

Dense Multi-Planar Scene Estimation from a 

Sparse Set of Images 

Alberto Argiles, Javier Civera and Luis Montesano 

I3A, Universidad de Zaragoza, Spain 

• 3D scene estimation from visual data is limited, in most cases, to a sparse 

set of salient points. 

• Robots need dense scene estimations in order to properly interact with a 

3D world. 

• We present an algorithm to densify a 

sparse point-based scene estimation for 

Manhattan-like environments. 

• The algorithm combines robust multimodel 

estimation and geometric and 

photometric tests again the visual data. 

Input images Dense multi-planar 3D scene estimation 

11:00–11:15 ThBT6.7 

Real-time Step Edge Estimation Using Stereo 

Images for Biped Robot 

Minami Asatani 

The Fundamental Technology Research Center, Honda R&D Co. Ltd, Japan. 

Shigeki Sugimoto and Masatoshi Okutomi 

Dept. of Mech. and Control Eng., Tokyo Institute of Technology, Japan. 

• A step edge estimation method using 

stereo images for the biped robots with 

the ability of the overhanging footstep. 

• The overhanging footstep: advantageous 

in terms of smooth walking and 

relaxation of restrictions on gait planning. 

• Pre-processing: Occupancy grid 

segmentation & fast direct image 

alignment for estimating the plane 

parameters of each step. 

• Step edge estimation: By our image 

segmentation technique minimizing a 

cost composed of the sum of pixel value 

differences of stereo images. 

• Optional use of uncalibrated projector for 

texture-less surfaces. 

The overhanging 

footstep 

The overhanging footstep and 

edge estimation results. 

10:55–11:00 ThBT6.6 

Plenoptic Flow: Closed-Form Visual 

Odometry for Light Field Cameras 

Donald G. Dansereau, Ian Mahon, 

Oscar Pizarro and Stefan B. Williams 


• Classic approaches to visual odometry are iterative and/or complex 

• We present a closed-form, featureless solution to visual odometry for 

planar camera arrays, a.k.a. “light field cameras” 

• Three solutions capable of estimating a camera’s 6-DOF trajectory in a 

closed-form manner are presented 

• Computation is simple and results are competitive with SIFT+RANSAC 

11:15–11:30 ThBT6.8 

Exploiting Motion Priors in Visual Odometry 

for Vehicle-Mounted Cameras 

with Non-holonomic Constraints 

Davide Scaramuzza 1 , Andrea Censi 2 , and Kostas Daniilidis 1 

1 GRASP Lab, University of Pennsylvania 

2 California Institute of Technology 

• We present an algorithm (MOBRAS) for 

outlier removal which is not based on 

RANSAC 

• Contrary to RANSAC, motion hypotheses are 

not generated from randomly-sampled point 

correspondences, but from a “proposal 

distribution” that is built by exploiting the 

vehicle non-holonomic constraints 

• We show that not only is the proposed 

algorithm significantly faster than RANSAC, 

but that the returned solution may also be 

better in that it favors the underlying motion 

model of the vehicle, thus overcoming the 

typical limitations of RANSAC 


–198–


Medical Robots & Systems 

Chair Pierre Dupont, Children's Hospital Boston, Harvard Medical School 

Co-Chair Jung Kim, KAIST 

10:00–10:15 ThBT7.1 

Identification of a hybrid model for simulation of 

the instrument/trocar interaction force 

J. Verspecht, L. Catoire, S. Torfs and M. Kinnaert 

Control Engineering and System Analysis Department 

Université libre de Bruxelles (ULB), Belgium 

• Design of a simulator for the slave robot 

interacting with its environment in 

minimally invasive robotic surgery. 

• Short introduction to the Extended 

Maxwell-Slip (EMS) model, a 2-state 

hybrid friction model including a 

deformation and a sliding phase 

• Identification procedure for the EMS 

model using a PWARX identification 

techniques namely the clustering based 

technique. 

• Identification and validation are achieved 

using experimental data recorded on a 

dedicated test setup. 

10:30–10:45 ThBT7.3 

Bio-inspired Active Soft Orthotic Device 

for Ankle Foot Pathologies 

Yong-Lae Park1 , Bor-rong Chen2 , Diana Young1 , Leia Stirling1 , 

Robert J. Wood1,2 , Eugene Goldfield1,3 and Radhika Nagpal1,2 1Wyss Institute for Biologically Inspired Engineering, Harvard University, USA 

2School of Engineering and Applied Science, Harvard University, USA 

3Children’s Hospital Boston, USA 

• Active soft ankle-foot 

orthotic device 

• Biologically inspired 

design based on a 

musculoskeletal system 

of a human foot and leg 

• Soft sensor and 

actuator materials with 

no rigid frame structure 

• For treating gait 

pathologies associated 

with neuromuscular 

disorders 

10:15–10:30 ThBT7.2 

A Fast Classification System For Decoding 

of Human Hand Configurations 

Using Multi-Channel sEMG Signals 

Myoung Soo Park, Keehoon Kim and Sang Rok Oh 

Center of Human-centered Interaction and Robotics 

Korea Institute of Science and Technology (KIST), Korea 

• We propose a novel fast classification 

system that allows real-time decoding of 

human movement intentions from multichannel 

sEMG signals 

• For extracting small-dimensional and 

discriminative features, CA-PCA (Classaugmented 

PCA) are applied to RMS 

(root-mean-squared) sEMG signals 

• As a fast classifier, ELM (Extreme 

Learning Machine) are selected and used 

• From 28,000 8-channel sEMG of 4 actions 

and rest, feature and classifier are learned 

in less than 10 secs and can classify 

280,000 test data with 91.59% accuracy 

10:45–10:50 ThBT7.4 

A Preliminary Experiment for Transferring 

Human Motion to a Musculoskeletal Robot 

― Decomposition of Human Running based on Muscular Coordination ― 

Taiki Iimura, Keita Inoue, Hang T.T. Pham, 

Hiroaki Hirai, and Fumio Miyazaki 

Mechanical Science and Bioengineering, Osaka University, Japan 

• The electromyographic signals of 8 leg 

muscles during human running were 

measured. 

• Two parameters related to kinematics or 

kinetics are defined from EMG data. 

• Principal Component Analysis 

decomposes human running into only two 

muscle coordination patterns. 

• The kinematic meanings of the extracted 

EMG patterns were visualized by a 

musculoskeletal leg robot. 


–199– 

Robot movements corresponding 

to the extracted EMG patterns


Medical Robots & Systems 

Chair Pierre Dupont, Children's Hospital Boston, Harvard Medical School 

Co-Chair Jung Kim, KAIST 

10:50–10:55 ThBT7.5 

Heart Motion Simulator for Motion 

Compensation 

Renat Iskakov, Martin Groeger and Gerd Hirzinger 

Institute of Robotics and Mechatronics, 


• Heart motion simulator 

(HMS) to accurately 

simulate 3D translational 

motion trajectories of 

human beating heart 

• System description 

including design, 

kinematics, and closedloop 

dynamics 

• Impedance control structure for accurate simulation and future 

interaction with medical tools 

• Experiments show the capability of the HMS to simulate different lowamplitude 

translational motion trajectories 

11:00–11:15 ThBT7.7 

New Approach for Abnormal Tissue Localization 

with Robotic Palpation and Mechanical Property 

Characterization 

Bummo Ahn, Yeongjin Kim, and Jung Kim 

Department of Mechanical Engineering, KAIST, Korea 

• Abnormal tissue localization 

- Robotic palpation: scanning & sweeping 

- FEM-based mechanical property characterization. 

New approach for abnormal tissue localization 

Palpation experiment 

Mechanical property characterization 

10:55–11:00 ThBT7.6 

MRI-Powered Actuators for Robotic Interventions 

Panagiotis Vartholomeos and Pierre E. Dupont 

Cardiovascular Surgery, Children’s Hospital Boston, 

Harvard Medical School, USA 

Lei Qin 

Department of Radiology, Brigham & Women’s Hopsital, Harvard 

Medical School, USA 

• Novel actuation technology for 

robotically assisted MRI-guided 

interventional procedures 

• Actuators are both powered and 

controlled by the MRI scanner 

• Design concept and performance 

limits are described and derived 

analytically 

• Experiments in a clinical MR scanner 

are used to demonstrate the capability 

of the approach for needle biopsies 


–200– 

MRI-powered 

actuator 

MRI-powered actuator performs 

needle biopsy in porcine heart. 

11:15–11:30 ThBT7.8 

Position Estimation of an Epicardial Crawling 

Robot on the Beating Heart by Modeling of 

Physiological Motion 

Nathan Wood and Cameron Riviere 


Diego Moral del Agua 

University of Valladolid, Spain 

Marco Zenati 

BHS Department of Cardiothoracic Surgery, Harvard Medical School, USA 

• HeartLander robot provides therapies to 

the heart in a minimally invasive manner 

• Modeling of cardiac and respiratory 

motion enables more accurate position 

estimation of robot on heart 

• Fourier series models of physiological 

motions are fit using an Extended Kalman 

Filter (EKF) framework 

• Improves localization over previous 

filtering techniques and enables 

locomotion synchronization with 

physiological cycles on heart phantom


Search & Rescue Robots 

Chair Timothy H. Chung, Naval Postgraduate School 

Co-Chair Geoffrey Hollinger, Univ. of Southern California 

10:00–10:15 ThBT8.1 

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10:30–10:45 ThBT8.3 

Optimal Deployment of Robotic Teams for 

Autonomous Wilderness Search and Rescue 

Ashish Macwan, Goldie Nejat, and Beno Benhabib 

Department of Mechanical and Industrial Engineering, University of Toronto, 

Canada 

• An optimal re-deployment method for autonomous robotic Wilderness 

Search and Rescue (WiSAR) is presented 

• WiSAR challenges include: moving lost person, growing search area, 

and varying terrain 

• The proposed method addresses initial deployment as well as 

subsequent online re-deployment 

• The novel concept of iso-probability curves is used to predict and 

represent the lost person’s probable location 

• Optimal deployment / re-deployment is based on minimizing search time 

and maximizing probability of success 

• A simulated WiSAR search scenario demonstrates the method 

10:15–10:30 ThBT8.2 

Searching for Multiple Targets Using 

Probabilistic Quadtrees 

Stefano Carpin and Derek Burch 

School of Engineering, University of California - Merced, USA 

Timothy H. Chung 

Dept. of Systems Engineering, Naval Postgraduate School - Montery, USA 

• We extend Probabilistic Quadtrees for 

search to deal with an arbitrary, unknown 

number of targets; 

• An information gain function is defined to 

guide the search; 

• An entropy based criterion terminates the 

search; 

• We theoretically show that in the limit the 

hierarchical representation converges to a 

uniform grid; 

• Extensive simulation show hierarchical 

search largely outperforms uniform 

approaches. 

10:45–10:50 ThBT8.4 

Development of the high strength retractable 

skin and the closed type crawler vehicle 

Takeshi Aoki and Takahiro Karino 

Advanced robotics, Chiba Institute of Technology, Japan 

Hiroyuki Kuwahara 

SUSTAINable Robotics, Japan 

• Proposal and development of the high 

strength retractable skin 

• Proposal a new concept of crawler vehicle 

and mechanisms covered by the skin 

• Development of the mono crawler vehicle, 

named “Slug Crawler Vehicle: SCV” 

• SCV has water and dust proof abilities by 

the Flexible crawler belt 

• Verification of its effectiveness by basic 

experiments of the prototype model 


–201– 

The closed type flexible 

crawler vehicle


Search & Rescue Robots 

Chair Timothy H. Chung, Naval Postgraduate School 

Co-Chair Geoffrey Hollinger, Univ. of Southern California 

10:50–10:55 ThBT8.5 

Human-in-the-Loop: MPC for Shared Control of a 

Quadruped Rescue Robot 

Rahul Chipalkatty, Hannes Daepp, 

Magnus Egerstedt*, and Wayne Book 

Mechanical Engineering, Electrical and Computer Engineering*, 


• Composition of human input with an 

automatic controller to ensure static 

rescue robot stability. 

• A hybrid control architecture used to 

implement the supporting gait. 

• Control applied to a rescue robot 

simulation with real-time human input from 

haptic joysticks. 

• Experimental results confirm the viability 

of control approach: strengths of both 

human and robot control. 

10:55–11:00 ThBT8.6 

Detection and Tracking of Road Networks in 

Rural Terrain Fusing Vision and LIDAR 

Michael Manz, Michael Himmelsbach, Thorsten Luettel 

and Hans-Joachim Wuensche 

Autonomous System Technology, University of the Bundeswehr Munich, 

Germany 

• Fusing 3D LIDAR data and camera 

images into an accumulated, colored 3D 

elevation map of the terrain 

• Model based estimation of the local road 

network geometry using a particle filter 

and multiple features within the map 

• Supervised learning is applied to assess 

the likelihood of particles 

• Enables our robot MuCAR-3 to 

autonomously navigate on dirt-road 

networks 


–202– 

Crossing detection


Intelligent Transportation Systems (Automotive) 

Chair Bernd Radig, TU Muenchen 

Co-Chair Juan Nieto, Univ. of Sydney, Australian Centre for Field Robotics 

10:00–10:15 ThBT9.1 

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10:30–10:45 ThBT9.3 

Autonomous Intersection Management: 

Multi-Intersection Optimization 

Matthew Hausknecht, Tsz-Chiu Au, and Peter Stone 

Department of Computer Science, University of Texas at Austin, USA 

• In a future of autonomous vehicles, 

intelligent traffic management systems 

such as Autonomous Intersection 

Management (AIM) are crucial 

• Exploring AIM for the first time on road 

networks, we: 

• Examine vehicle navigations 

strategies 

• Observe an instance of Braess’ 

Paradox 

• Dynamically reverse the direction of 

traffic along selected lanes 

• We quantify the improvements imparted 

by these agent-based traffic control 

methods 

Intersection in AIM simulator 

10:15–10:30 ThBT9.2 

Tracking Objects of Arbitrary Shape Using 

Expectation Maximization Algorithm 

Shuqing Zeng and Yuanhong Li 

Electrical and Controls Lab, GM R&D Center, USA 

Yantao Shen 

Department of Electrical and Biomedical Engineering, University of Nevada, 

USA 

• Address the general object tracking with 

arbitrary shape using rangefinders, which 

is a key module for an autonomous driving 

vehicle. 

• An Expectation Maximization (EM) 

algorithm with locally matching is 

proposed for motion estimation between 

two consecutive range images. 

• The complexity of the algorithm is O(N) 

with N the numbers of scan points. 

• Quantitative performance evaluation of the 

algorithm using a benchmarking vehicular 

data set. 

• Results of road tests show the 

effectiveness and efficiency of the system. 

10:45–10:50 ThBT9.4 

Evaluation of Different Approaches for Road 

Course Estimation using Imaging Radar 

Frederik Sarholz, Jens Mehnert, Jens Klappstein 

and Juergen Dickmann 

Group Research/Environment Perception, Daimler AG, Germany 

Bernd Radig 

Intelligent Autonomous Systems, Technische Universitaet Muenchen, Germany 

• Three approaches for road course 

estimation are evaluated based on 

several hundred kilometers of country 

roads: 

1. spatial derivative + lane free of radar 

echoes 

2. angle extraction of the road border + 

projection onto the vehicle’s course 

(performs up to 78% better than 1) 

3. usage of objects moving parallel to the 

road course 

(performs up to 209% better than 1) 

• The driven trajectory is taken as ground 

truth 


–203– 

x[m] 

160 

120 

80 

40 

0 

x[m] 

80 

40 

y[m] y[m] 

Left: estimation with second approach 

Right: estimation with third approach, 

the dots are the measured positions of 

the followed vehicle 

0


Intelligent Transportation Systems (Automotive) 

Chair Bernd Radig, TU Muenchen 

Co-Chair Juan Nieto, Univ. of Sydney, Australian Centre for Field Robotics 

10:50–10:55 ThBT9.5 

A Car Transportation System Using 

Multiple Mobile Robots: iCART II 

K. Kashiwazaki 1 , N. Yonezawa 1 , M. Endo 2 , K. Kosuge 1 , 

Y. Sugahara 1 , Y. Hirata 1 , T. Kanbayashi 3 , K. Suzuki 3 , 

K. Murakami 3 and K. Nakamura 3 

1 Department of Bioengineering and Robotics, Tohoku University, Japan 

2 Department of Mechanical Engineering, Nihon University, Japan 

3 IHI Transport Machinery Co., Ltd., Japan 

• This paper proposes a car transportation 

system, iCART II, based on “a-robot-fora-wheel” 

concept. 

• A prototype system, MRWheel (a Mobile 

Robot for a Wheel), is designed. 

• We propose a decentralized control 

algorithm for car transportation in 

coordination by using a leader-follower 

type multiple robot system. 

• The proposed control algorithm is applied 

to the car transportation system and the 

experimental results are reported. 

Mobile Base Module 

Connecting Module 

Lifter Module Omni-directional Wheel 

11:00–11:15 ThBT9.7 

Intelligent Driver Drowsiness Detection System 

Using Uncorrelated Fuzzy Locality Preserving 

Analysis 

Rami N. Khushaba, Sarath Kodagoda, Gamini Dissanayake 

ARC centre of Excellence for Autonomous Systems, Faculty of Engineering and 

Information Technology, University of Technology, Sydney (UTS), Australia. 

Sara Lal 

Department of Medical and Molecular Biosciences, Faculty of Science, 

University of Technology, Sydney (UTS), Australia. 

• Driver drowsiness is believed to account 

for 20% of all vehicle accidents on 

motorways and monotonous roads. 

• Problem: identify physiological 

associations between driver drowsiness 

and the corresponding patterns of EEG, 

EOG, and ECG. 

• Approach: A pattern recognition approach 

is adopted by proposing a novel feature 

extraction and reduction algorithm UFLPA. 

• Experiments on 31 participants provided a 

classification accuracy of ~95%. 

Driving simulation system 

10:55–11:00 ThBT9.6 

Probabilistic Road Geometry Estimation using a 

Millimetre-Wave Radar 

Andres Hernandez-Gutierrez, University of Sydney, Australian 

Centre for Field Robotics 

Juan Nieto, University of Sydney, Australian Centre for Field 

Robotics 

Tim Bailey, University of Sydney 

Eduardo Nebot, Unversity of Sydney 

11:15–11:30 ThBT9.8 

An Efficient Control Strategy for the Traffic 

Coordination of AGVs 

Roberto Olmi 

Elettric80, Italy 

Cristian Secchi and Cesare Fantuzzi 

DISMI, University of Modena and Reggio Emilia, Italy 

• Coordination of AGVs over a zone 

cotrolled layout 

• Coordination diagrams are used for 

representing possible collisions 

• A computationally efficient strategy for 

segments allocations is proposed 

• The traffic control strategy is tested over 

real industrial layouts 


–204–

Session ThCT1 Continental Parlor 1 Thursday, September 29, 2011, 14:45–16:15 

Manipulation 

Chair Mike Stilman, Georgia Tech. 

Co-Chair Ludovic Righetti, Univ. of Southern California 

14:45–15:00 ThCT1.1 

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15:15–15:30 ThCT1.3 

Distributed Generalization of 

Learned Planning Models in Robot PbD 

Rainer Jäkel, Sven R. Schmidt-Rohr, Rüdiger Dillmann 

Institute for Anthropomatics, Karlsruhe Institute of Technology, Germany 

Pascal Meißner 

FZI - Research Center for Information Technologies, Germany 

• Manipulation knowledge can be learned 

efficiently from human observation 

• Problem: how to determine a reasonable 

set of learning features? 

• Approach: distributed simulation and 

constrained motion planning to generate 

statistics about constraint inconsistencies 

• Goal: determine a maximal subset of task 

constraints, which allows the robot to 

solve a set of test problems 

15:00–15:15 ThCT1.2 

Push Planning for Object Placement on 

Cluttered Table Surfaces 

Akansel Cosgun, Tucker Hermans, Victor Emeli and Mike Stilman 

Center for Robotics and Intelligent Machines 


• A planner to find a sequence of 

pushes of tabletop objects to make 

room for a target object 

• Implemented in dynamic simulation 

• Efficient and applicable to practical 

domains 

(a) Initial State 

• Placement pose found by convolving object kernel 

over table and sampling among the least 

overlapping poses 

• Iterative Deepening Breadth-First-Search (IDBFS) 

used to try different placement poses and gradually 

allow more complex plans 


–205– 

7 

6 

4 

5 

(b) Plan 

6 

6 

7 

7 

7 

6 

4 

4 

5 

Area 5 is cleared 

15:30–15:35 ThCT1.4 

Learning Force Control Policies 

for Compliant Manipulation 

Mrinal Kalakrishnan * , Ludovic Righetti * , 

Peter Pastor * , and Stefan Schaal *† 

* CLMC Lab, University of Southern California, USA 

† Max-Planck-Institute for Intelligent Systems, Tübingen, Germany 

• Compliant actuation and control are 

critical for safe operation of robots in 

household environments. 

• Manipulation involves exerting forces on 

the world through contact interaction. 

• We learn control policies for both positions 

and forces, using the PI 2 reinforcement 

learning algorithm. 

• This allows compliant execution with 

increased robustness as opposed to stiff 

position control. 

• We use this method to learn dooropening 

and pen-grasping tasks on a 

Barrett WAM arm. 

4 

5 

5


Manipulation 

Chair Mike Stilman, Georgia Tech. 

Co-Chair Ludovic Righetti, Univ. of Southern California 

15:35–15:40 ThCT1.5 

Autonomous Indoor Aerial Gripping 

Using a Quadrotor 

Vaibhav Ghadiok, Jeremy Goldin and Wei Ren 

Electrical & Computer Engineering, Utah State University, USA 

• Custom-built quadrotor equipped with 

low-cost sensors: gyroscopes, 

accelerometers, camera, IR camera and 

sonar 

• Capabilities include visual SLAM, 

waypoint navigation, target search and 

aerial gripping while hovering 

• IR camera to locate target and 

underactuated passively compliant 

gripper for gripping 

• Nested PID controllers for attitude 

stabilization, altitude regulation, 

navigation and gripping 

15:45–16:00 ThCT1.7 

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15:40–15:45 ThCT1.6 

Shooting Manipulation System 

with High Reaching Accuracy 

Tomofumi Hatakeyama and Hiromi Mochiyama 

Department of Intelligent Interaction Technologies 

University of Tsukuba, Japan 

• We propose a shooting manipulation 

system with high reaching accuracy for 

manipulating a distant object 

• The proposed system has excellent 

performances such as high motion 

straightness, quickness and reaching 

accuracy 

• The end-effector rests at target point, then 

we clarify the mechanics of this motion 

• Realize quick capturing of a falling object 

at 700[mm] away with high success rate 


–206– 

Quick capturing motion 

16:00–16:15 ThCT1.8 

A Novel Approach to the Manipulation of Body-Parts 

Ownership Using a Bilateral Master-Slave System 

Masayuki Hara, Akio Yamamoto, and Toshiro Higuchi 

Dept. of Precision Engineering, The University of Tokyo, Japan 

Giulio Rognini and Hannes Bleuler 

Institute of Microengineering, EPFL, Switzerland 

Nathan Evans and Olaf Blanke 

Brain Mind Institute, EPFL, Switzerland 

• A novel tactile rubber hand illusion (RHI) 

paradigm with haptics/robotics technology 

is proposed for manipulating body-parts 

ownership 

• A 3-DOF master-slave system is designed 

to enable RHI experiments under active 

self-touch condition 

• The applicability of the proposed system is 

validated conducting a self-touch-enabled 

RHI experiment 

• The proposed approach has a possibility 

to answer current open questions about 

body representation in human brain 

Paintbrush attached on 

a slave device 

Rubber hand 

Paintbrush attached on 

a master device 

Participant 

Self-touch-enabled RHI paradigm 

with a master-slave system



Chair Jun Ota, The Univ. of Tokyo 

Co-Chair Rolf Johansson, Lund Univ. 

14:45–15:00 ThCT2.1 

Assembly Strategy Modeling and Selection for 

Human and Robot Coordinated Cell Assembly 

Fei Chen 1 , Kosuke Sekiyama 1 , Hironobu Sasaki 1 , Jian Huang 3 , 

Baiqing Sun 4 , Toshio Fukuda 1,2 

1 Department of Micro-nano Systems Engineering, Nagoya University, Japan. 

2 Center for Micro-nano Mechatronics, Nagoya University, Japan. 

3 Department of Control Science and Engineering, 

Huazhong University of Science and Technology, China. 

4 School of Electrical Engineering, Shenyang University of Technology, China. 

•A Dual Generalized Stochastic Petri Net 

(GSPN) model is built based on a practical 

assembly task for human and robot 

coordination by taking possible human and 

robot behaviors into consideration. 

• Based on GSPN, Monte Carlo method is 

carried out to study the time cost and 

payment cost for different assembly 

subtask scheduling strategies within 

human and robot. 

• Multiple-Objective Optimization (MOOP) 

method related Cost-effectiveness analysis 

is adopted to select the optimal assembly 

scheduling strategy. 

15:15–15:30 ThCT2.3 

Reusable Hybrid Force-Velocity controlled 

Motion Specifications with executable DSL 

Markus Klotzbuecher, Ruben Smits, 

Herman Bruyninckx, Joris De Schutter 



• An approach for modeling reusable 

robotic tasks using Domain Specific 

Languages (DSL) 

• Models of force-velocity controlled 

motions are composed in finite state 

machines 

• DSL are used to separate platform 

independent from platform specific 

aspects 

• Illustrated by an alignment task 

executed on a KUKA LWR and a PR2 

robot 

Alignment task executed by a 

KUKA LWR robot 

15:00–15:15 ThCT2.2 

Stereographic projection for industrial 

manipulator tasks: Theory and experiments 

Magnus Bjerkeng and Kristin Y. Pettersen 

Department of Engineering Cybernetics, NTNU, Norway 

Erik Kyrkebø 

SINTEF ICT Applied Cybernetics, Norway 

• A pseudoinverse controller using a 

task representation based on the 

stereographic projection, applied to 

a dynamic surveillance problem, is 

presented 

• A comparative analysis between 

similar task representations is 

provided 

• A qualitative analysis of the global 

dynamics is presented and the 

existence and uniqueness of 

singularities is determined 

• Experiments using industrial 

manipulators are presente. 

15:30–15:35 ThCT2.4 

Entropy-Based Motion Selection 

for Touch-Based Registration 

Using Rao-Blackwellized Particle Filtering 

Yuichi Taguchi, Tim K. Marks, and John R. Hershey 

Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA 

• 6-DOF robot-to-object registration using 

a sequence of touches 

• Efficient online estimation using Rao- 

Blackwellized Particle Filtering (RBPF) 

• Entropy-based motion selection to 

determine optimal next touch location 

• Comparison of several entropy 

measures for RBPF 

• Applications: Fully automatic registration 

and human-guided registration 


–207– 

Probe 

Particle Distribution 

on Object Model 

Force 

Sensor 

Object



Chair Jun Ota, The Univ. of Tokyo 

Co-Chair Rolf Johansson, Lund Univ. 

15:35–15:40 ThCT2.5 

Manipulator System Selection Based on 

Evaluation of Task Completion Time and Cost 

Yanjiang Huang, Lounell B. Gueta and Jun Ota 

Research into Artifacts, Center for Engineering, The University of Tokyo, Japan 

Ryosuke Chiba 

Faculty of System Design, Tokyo Metropolitan University, Japan 

Tamio Arai 

Department of Precision Engineering, The University of Tokyo, Japan 

Tsuyoshi Ueyama 

DENSO WAVE INCORPORATED, Japan 

Masao Sugi 

Department of Mechanical Engineering and Intelligent Systems, 

University of Electro-Communications, Japan 

• Multiple objective particle swarm 

optimization is utilized to search for 

appropriate manipulator systems. 

• Location optimization and motion 

coordination are utilized to calculate task 

completion time of manipulator system. 

• Relative cost is utilized to calculate cost of 

manipulator system. 

• Particle swarm optimization and nearest 

neighborhood algorithm are employed in 

location optimization and motion 

coordination. 

System selection Start 

Select manipulator system, s 

Location optimization 

Select location of positioning table, L 

Motion coordination 

Update location 

No 

Terminate select L ? 

Yes 

Calculate cost 

Update manipulator system 

Terminate select s ? 

Yes 

End 

No 

Proposed method for 

manipulator system selection 

15:45–16:00 ThCT2.7 

Dynamics identification of industrial robots 

using contact force for the IDCS control 

Kengo AOKI , Gentiane VENTURE , Yasutaka TAGAWA 

Department of Mechanical Systems Engineering, 

Tokyo University of Agriculture and Technology, Tokyo, Japan 

• The application of identification of 

the robot using the base link 

information 

• The concept of the IDCS (Inverse 

Dynamics Compensation via 

‘Simulation of feedback control 

systems’) controller 

• The simulation results of 

comparison of the IDCS controller 

performance with PID controller 

Weight with adding 

acceleration sensor 

Motor with 

an encoder 

Top: The controlled object 

Bottom: The cross validation of 

� � � identification result 

15:40–15:45 ThCT2.6 

Modeling and Control of a Piezo-Actuated 

High-Dynamic Compensation Mechanism for 


B. Olofsson, O. Sörnmo, A. Robertsson & R. Johansson 

Dept. of Automatic Control, Lund University, Sweden 

U. Schneider & A. Puzik 

Fraunhofer Institute for Manufacturing and Engineering, Germany 

• Piezo-actuated compensation mechanism 

for usage together with industrial robots 

during machining operations 

• High-precision milling despite limited 

stiffness and accuracy of the robot 

• Modeling of the compensation mechanism 

using subspace-based identification 

methods and model-based control 

scheme 

• Experimental evaluations of the control 

scheme performed on a prototype of the 

mechanism are presented 


–208– 

Industrial robot together with the 

compensation mechanism. 

16:00–16:15 ThCT2.8 

A Fast Distributed Auction and Consensus Process 

Using Parallel Task Allocation and Execution 

G.P. Das, T.M. McGinnity, S.A. Coleman and L. Behera 

Intelligent Systems Research Centre, University of Ulster, United Kingdom 

• Distributed auction and consensus for 

task allocation in a multi robot system 

• Task allocation is carried out in parallel to 

execution of already allocated tasks 

• Task allocation in highly dynamic 

scenarios is better than other distributed d 

auction based approaches 

• Performance analysis 

• static cases where all robots and 

tasks are active from the start 

• dynamic case where new tasks are 

activated during the execution 

• Faster task allocation in both static and 

dynamic scenarios 

Figure: Task allocation module in 

a single robot. Similar modules 

run in each robot of the multi 

robot system.


Marine Systems I 

Chair Stephen Barkby, Univ. of Sydney 

Co-Chair Terry Huntsberger, Jet Propulsion Lab. 

14:45–15:00 ThCT3.1 

Flexible Formation Control Realistic Underactuated 

Autonomous Surface Vessels 

Bradley Bishop, United States Naval Academy 

15:15–15:30 ThCT3.3 

Safe Maritime Navigation with COLREGS 

Using Velocity Obstacles 

Yoshiaki Kuwata, Michael T. Wolf, Dimitri Zarzhitsky, 

and Terrance L. Huntsberger 

Mobility and Robotics Systems, Jet Propulsion Laboratory, 


• In maritime navigation, all vessels must obey COLREGS (“rules of the 

road”), which requires specific type of maneuvers (e.g., need to overtake 

from the starboard side). 

• Developed a motion planner that 

incorporates such logic for USVs 

(Unmanned Surface Vehicles). 

• Velocity Obstacles allow us to 

express them naturally in the 

velocity space. 

• Demonstrated in simulation and 

on the water three COLREGS 

behaviors: crossing, overtaking, 

and head-on. 

USV in a crossing situation 

15:00–15:15 ThCT3.2 

Toward Automatic Classification of Chemical 

Sensor Data from Autonomous Underwater Vehicles 

Michael V. Jakuba 1 , Daniel Steinberg 1 , James C. Kinsey 2 , Dana 

R. Yoerger 2 , Richard Camilli 2 , Oscar Pizarro 1 , Stefan B. Williams 1 

1 Australian Centre for Field Robotics, University of Sydney, Australia 

2 Woods Hole Oceanographic Institution, USA 

• Classification of nearly raw 

chemical sensor data from 

AUVs can aid follow-on 

analysis by both humans 

and machines. 

• The Variational Dirichlet 

Process (VDP) was used to 

cluster chemical sensor 

data from the Sentry AUV. 

• The VDP executes rapidly, 

accepts data from multiple 

sensors, and does not 

require operators to specify 

the number of classes a 

priori. 


–209– 

Probability of membership in the plume class 

derived from chemical sensor data collected by 

the Sentry AUV. A plume is evident trailing off to 

the west from the Deepwater Horizon site in the 

northeast corner of the plot. 

15:30–15:35 ThCT3.4 

A Novel Propulsion Method 

of Flexible Underwater Robots 

Jun Shintake, Aiguo Ming, Makoto Shimojo 

Department of Mechanical Engineering and Intelligent Systems 

The University of Electro-Communications, Japan 

• Proposal of the new type of 

propulsion method 

• Development of prototypes 

• Experiments and analysis


Marine Systems I 

Chair Stephen Barkby, Univ. of Sydney 

Co-Chair Terry Huntsberger, Jet Propulsion Lab. 

15:35–15:40 ThCT3.5 

On Adaptive Underwater Object Detection 

David P. Williams 

NATO Undersea Research Centre, Italy 

• A new, fast algorithm for the detection of 

underwater objects in sonar imagery is 

proposed. 

• The algorithm also detects the presence 

of, and estimates the orientation of, sand 

ripples. 

• The method is adaptively tailored to the 

environmental characteristics of the 

sensed data that is collected in situ. 

• Ways to exploit the findings and adapt 

AUV surveys for optimized detection 

performance are suggested. 

Synthetic aperture sonar image 

with man-made objects 

and sand ripples. 

15:45–16:00 ThCT3.7 

Pitch and Roll Control Using Independent 

Movable Floats for Small Underwater Robots 

Norimitsu Sakagami*, Tomohiro Ueda** 

Mizuho Shibata*** and Sadao Kawamura** 

*Department of Navigation and Ocean Engineering, Tokai University, Japan 

**Department of Robotics, Ritsumeikan University, Japan 

***Department of Intelligent Mechanical Engineering, Kinki University, Japan 

• We propose a pitch and roll control 

system based on a float-shift mechanism 

• Two independent movable floats control 

the center of buoyancy of a robot 

• Numerical results show that the proposed 

system contributes to change and keep 

the attitude of the robot 

• The experimental test was also conducted 

in a test tank 

Human-sized Underwater Robot 

• The experimental results show the equipped with an attitude control system 

performance of pitch and roll angle control based on a float-shift mechanism 

15:40–15:45 ThCT3.6 

Modeling, Simulation, and Performance of a 

Synergistically Propelled Ichthyoid 

Paul Strefling, Aren Hellum and Ranjan Mukherjee 


Michigan State University, 

East Lansing, Michigan, United States 

Images of the submersible captured over a period of one second immediately after 

starting from rest. 

Synergistically Propelled Ichthyoid is a submersible with a fluid-conveying 

flexible tail. 

Propulsion is achieved by combining jet action and oscillatory tail motion 

induced by the jet. 

Analytical and numerical methods are employed to solve two models of the 

Synergistically Propelled Ichthyoid. 

Experiments were performed which qualitatively verify the models. 


–210–

Session ThCT4 Continental Ballroom 4 Thursday, September 29, 2011, 14:45–16:15 

Symposium: (Self-)assembly from the Nano to the Macro Scale: State of the Art and Future Directions 

Chair Nikolaus Correll, Univ. of Colorado at Boulder 

Co-Chair Mark Yim, Univ. of Pennsylvania 

14:45–15:00 ThCT4.1* 

(Self-)assembly from the nano to the macro scale 

Nikolaus Correll, University of Colorado at Boulder 

M. Ani Hsieh, Drexel University 

Mark Yim, University of Pennsylvania 

15:15–15:30 ThCT4.3 

Self-Assembly for Maximum Yields Under 

Constraints 

Michael J. Fox and Jeff S. Shamma 

School of Electrical and Computer Engineering, Georgia Institute of Technology, 

United Stated of America 

• We present an algorithm that, given 

any target tree, synthesizes 

reversible self-assembly rules that 

provide a maximum yield in the 

sense of stochastic stability. 

• If the reversibility constraint is 

relaxed then the same algorithm 

can be trivially modified so that it 

converges to a maximum yield 

almost surely. 

• We examine the conservatism of 

this technique by considering its 

implications for the internal states 

of the system. 

We use graph grammars to 

describe our algorithm. 

15:00–15:15 ThCT4.2 

Enhanced Directional Self-Assembly 

based on Active Recruitment and Guidance 

Nithin Mathews1 , Anders Lyhne Christensen2 , 

Rehan O’Grady1 , Philippe Rétornaz3 , Michael Bonani3 , 

Francesco Mondada3 , and Marco Dorigo1 1IRIDIA, Université Libre de Bruxelles, Brussels, Belgium 

2Instituto de Telecomunicações, Lisbon, Portugal 

3EPFL-LSRO Ecole Polytechnique Fédérale de Lausanne, Switzerland 

• Self-assembling robots 

• Grow specific morphologies 

• We propose a new mechanism 

to create directional 

connections between robots 

• Real-robot experiments 

15:30–15:35 ThCT4.4 

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–211– 

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Symposium: (Self-)assembly from the Nano to the Macro Scale: State of the Art and Future Directions 

Chair Nikolaus Correll, Univ. of Colorado at Boulder 

Co-Chair Mark Yim, Univ. of Pennsylvania 

15:35–15:40 ThCT4.5 

Self-Assembly of Modular Robots from Finite 

Number of Modules Using Graph Grammars 

Vijeth Rai and Nikolaus Correll 

University of Colorado at Boulder, USA 

Anne van Rossum 

Almende Research and TiCC, Tilburg University , The Netherlands 

• Stochastic Self-Assembly of 

modular robots when number of 

available building blocks is limited or 

exact 

• Stochastic approach provides 

scalability and robustness, while not 

requiring global localization 

• Combining assembly with 

disassembly leads to 100% yield 

• Broadcast communication 

accelerates assembly 

Hexapod assembly from 

“Replicator” modules 

15:45–16:00 ThCT4.7 

Structure synthesis on-the-fly in a modular robot 

Shai Revzen, Mohit Bhoite, Antonio Macasieb, and Mark Yim 

School of Engineering and Applied Science 


• Modular robots promise robots created 

on-the-fly to suite tasks not known in 

advance of deployment 

• We demonstrate a remote controlled mobile 

synthesizer capable of creating structural 

members for linking actuated modules into 

arbitrary custom robots 

• Material is a foam with x28 expansion; 

creating robots larger than synthesizer cart 

• Also shown application to hazard disposal 

and rescue / urban warfare 

• Videos and more details at 

http://www.modlabupenn.org/foambot-videos 

Synthesizer cart & two mobile 

clusters (top), a quadruped and a 

snake 

15:40–15:45 ThCT4.6 

Constrained Task Partitioning for 

Distributed Assembly 

James Worcester, Josh Rogoff, and M. Ani Hsieh 

Scalable Autonomous Systems Lab 

Mechanical Engineering & Mechanics 

Drexel University, USA 

• We present an algorithm to partition 2 and 

3-D structures into N separate assembly 

tasks that can be executed in parallel. 

• The algorithm achieves a partitioning that 

minimizes the workload imbalance 

between the robots. 

• The algorithm consists of three phases: 1) 

an initial allocation, 2) workload balance, 

and 3) generation of parallel assembly 

plans. 

• We present simulation and experimental 

results. 


–212– 

Two robots assembling the 3-D 

structure (bottom) in parallel. 

16:00–16:15 ThCT4.8 

Constraint-Aware Coordinated Construction 

of Generic Structures 

David Stein, T. Ryan Schoen, Daniela Rus 

Massachusetts Institute of Technology, USA 

• Constraint-aware decentralized approach to construction 

• Novel algorithm for partitioning sets of point masses in 

• Extension of previous work to conform to physical constraints of 

reachability and mid-construction stability 

Simulated parallel construction of a model airplane by three robots 

conforming to physical and reachability constraints


Symposium: Humanoid Applications 


Co-Chair Shigeki Sugano, Waseda Univ. 

14:45–15:00 ThCT5.1* 

Semi-Plenary Invited Talk: Humanoid Robotics 

Research: Retrospection and Prospects 

Tamim Asfour, Karlsruhe Institute of Technology (KIT) 

15:15–15:30 ThCT5.3 

Actuation Requirements for Hopping and 

Running of the Musculoskeletal Robot 

BioBiped1 

Katayon Radkhah and Oskar von Stryk 

Department of Computer Science, Technische Universität Darmstadt, Germany 

• Approach to determine the actuation 

requirements for given motions for a 

electrically driven bipedal robot 

• Integration of human-like series-elastic 

structures to mimic the main human leg 

muscles 

• Step-by-step process to compute the 

control signals for computer-generated 

hopping and human running motions 

• Good agreement of the simulated ground 

reaction forces with the typical human 

single-humped patterns 

Rigid kinematic joint-link structure 

(left) versus compliant robot 

actuated by series elastic 

structures (right) performing 

hopping and running motions 

15:00–15:15 ThCT5.2 

Semi-Plenary Invited Talk: Humanoid Robotics 

Research: Retrospection and Prospects 

Tamim Asfour, Karlsruhe Institute of Technology (KIT) 

15:30–15:35 ThCT5.4 

Combined Intention, Activity, and Motion 

Recognition for a Humanoid Household Robot 

Dirk Gehrig1 , Peter Krauthausen1 , Lukas Rybok1 , 

Hildegard Kuehne1 , Uwe D. Hanebeck1 , Tanja Schultz1 , 

and Rainer Stiefelhagen1 2 

1 Institute for Anthropomatics, 

Karlsruhe Institute of Technology (KIT), Germany 

2 Fraunhofer IOSB, Karlsruhe, Germany 

• Multi-Level approach to intention, 

activity, and motion recognition 

• Video-based on-line and real-time 

recognition for a humanoid robot 

• Key ideas 

• Complimentary recognizers 

• Extensible model with consistent 

uncertainty treatment 

• Experimental validation on realworld 

data set of complex kitchen 

tasks, e.g. Lay Table, Prepare Meal 

Robot System 

State Estimation 


–213– 

Intention 

Motion Activity 

Human and environment 

Model of Human‘s Rationale 

Domain 

Knowledge 

Robot Control 

Sensors Actuators


Symposium: Humanoid Applications 


Co-Chair Shigeki Sugano, Waseda Univ. 

15:35–15:40 ThCT5.5 

An Expected Perception Architecture using 3D 

Reconstruction for a Humanoid Robot 

N. Moutinho 1 , N. Cauli 2 , E. Falotico 2 , R. Ferreira 1 , J. Gaspar 1 , 

A. Bernardino 1 , J. Santos-Victor 1 , P. Dario 2 , C. Laschi 2 

1 Institute for Systems and Robotics, IST/UTL, Portugal 

2 The BioRobotics Institute, SSSA, Italy 

• Architecture: Anticipatory Visual 

Perception (AVP) performs Expected 

Perception (EP) 

• EP uses proprioception information to 

predict visual changes and detect 

unexpected visual events 

• EP allows saving computational 

resources by doing localized 3D 

reconstruction instead of full scene 

reconstruction 

15:45–16:00 ThCT5.7 

Development of Whole-body Humanoid "Pneumat- 

BS" with Pneumatic Musculoskeletal System 

Keita Ogawa 

Dept. of Adaptive Machine Systems,Osaka Univ., Japan 

Kenichi Narioka and Koh Hosoda 

Dept. of Multimedia Engineering,Osaka Univ,. Japan 

• We developed whole-body 

musculoskeletal humanoid “Pneumat-BS” 

• The robot is drivened by pneumatic 

artificial muscles 

• The robot has whole-body flexibility and 

whole-body coordination 

• We conducted a standing experiment 

using a simple feedback control and 

evaluated the stability 

• We conducted a walking experiment using 

a feedforward controller with human 

muscle activation patterns and confirmed 

that the robot was able to perform the 

dynamic task 

A musculoskeletal humanoid 

robot "Pneumat-BS" 

15:40–15:45 ThCT5.6 

Multilayer Control of Skiing Robot 

Tadej Petrič, Bojan Nemec, Jan Babič and Leon Žlajpah 

Department for automation, biocybernetics and robotics, 

Jožef Stefan Institute, Slovenia 

• Novel method for ensuring stability of a 

skiing robot on a previously unknown ski 

slope. 

• Multilayer control where the primary task 

(stability) is only observed when, the 

secondary task (direction) would lead into 

instability. 

• The effectiveness of the proposed method 

is shown in both simulation in the virtual 

reality environment as well as on the real 

robot, i.e. hardware-in-the-loop. 

16:00–16:15 ThCT5.8 

Autonomous Climbing of Spiral Staircases 

with Humanoids 

Stefan Oßwald, Attila Görög, 

Armin Hornung, and Maren Bennewitz 


• Our Nao humanoid autonomously climbs 

up complex staircases 

• The robot uses a 2D laser range finder to 

globally localize itself in a 3D model 

• The pose is refined by comparing 

detected edges in monocular camera 

images to model edges 

• The robot accurately aligns with the next 

step and reliably climbs up a spiral 

staircase consisting of 10 steps 


–214–


Symposium: Computer Vision for Robotics 

Chair Dieter Fox, Univ. of Washington 

Co-Chair José Neira, Univ. de Zaragoza 

14:45–15:00 ThCT6.1* 

Semi-Plenary Invited Talk: Learning to Recognize 

Objects Despite Novel Environments and Sensors 

Trevor Darrell, UC Berkeley 

15:15–15:30 ThCT6.3 

RGB-D Object Discovery 

Via Multi-Scene Analysis 

Evan Herbst, Xiaofeng Ren, Dieter Fox 

Computer Science, University of Washington, USA 

Intel Research Seattle, Seattle, USA 

• Represent a geometric map as static 

background + movable objects 

• Point-based change detection algorithm 

for segmentation and matching 

• Improve on 2-D feature-based matching 

and on 3-D ICP-based matching 

15:00–15:15 ThCT6.2 

Semi-Plenary Invited Talk: Learning to Recognize 

Objects Despite Novel Environments and Sensors 

Trevor Darrell, UC Berkeley 

15:30–15:35 ThCT6.4 

Online Learning for Automatic 

Segmentation of 3D Data 

Federico Tombari Luigi Di Stefano 

Simone Giardino 

DEIS, University of Bologna, Italy 

• Semantic segmentation of indoor/outdoor 

scenes based on standard classifiers and 

a Markov Random Field-based grouping 

stage 

• Online learning approach: new samples 

are continuously selected from incoming 

data in an unsupervised way to improve 

the learning model 

• We experimentally demonstrate 

performance improvements with different 

classifiers (NN, SVM) as well as 3D data 

sources (CAD/synthetic, LIDAR, Kinect) 


–215– 

Semantic segmentation of a 

complex scene acquired by a 

Microsoft Kinect sensor


Symposium: Computer Vision for Robotics 

Chair Dieter Fox, Univ. of Washington 

Co-Chair José Neira, Univ. de Zaragoza 

15:35–15:40 ThCT6.5 

Shape-Based Depth Image to 3D Model Matching 

and Classification with Inter-View Similarity 

Walter Wohlkinger, Markus Vincze 

Vision4Robotics Group, 

Automation and Control Institute, 

Vienna University of Technology, Austria 

• 3D Shape based matching of depth 

images acquired with Kinect to 3D models 

from the Web 

• Matching against multiple views 

significantly boosts classification rate 

• Adaptation and optimization of shape 

descriptors enables real-time classification 

• Easy and fast learning of new categories 

Classification of everyday objects 

for robotic applications 

15:45–16:00 ThCT6.7 

Perception for the Manipulation of Socks 

Ping Chuan Wang, Stephen Miller, Mario Fritz, 

Trevor Darrell, Pieter Abbeel 

University of California, Berkeley, USA 

• Provide perceptual tools necessary 

for robotic sock manipulation 

• Visually infer configuration of 

possibly bunched or inside-out socks 

• Combine local texture features into 

global model 

• Visually match pairs of socks 

• Implemented on the PR2 

15:40–15:45 ThCT6.6 

Model for Unfolding Laundry using 

Interactive Perception 

Bryan Willimon, Stan Birchfield, and Ian Walker 


Clemson University, USA 

• We present an algorithm for automatically 

unfolding a piece of clothing. 

� Our results show that, at most, the 

algorithm flattens out a piece of cloth with 

11.1% to 95.6% of the canonical position. 

� In this paper, we use peak ridges, 

continuity of a surface, and corner locations 

in our model to calculate grasp locations 

and movement orientations. 

16:00–16:15 ThCT6.8 

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–216– 

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Exoskeleton Robots & Gait Rehabilitation 

Chair Bernard Espiau, INRIA 

Co-Chair Constantinos Mavroidis, Northeastern Univ. 

14:45–15:00 ThCT7.1 

Design and Control of a Robotic Lower 

Extremity Exoskeleton for Gait Rehabilitation 

Ozer Unluhisarcikli, Maciej Pietrusinski, 

Brian Weinberg, Constantinos Mavroidis 

Dept. of Mechanical & Industrial Engineering, Northeastern University, USA 

Paolo Bonato 

Spaulding Rehabilitation Hospital, USA 

• Design and control of an active knee 

rehabilitation orthotic system (ANdROS) ) is 

presented. 

• ANdROS is designed as a wearable and d 

portable assistive tool for gait 


• A corrective force field that reinforces a 

desired gait pattern is applied to the 

patient’s impaired leg around the knee 

joint via an impedance controlled 

exoskeleton. 

• The impedance controller is synchronized ed 

with the patient’s walking phase which is s 

estimated from the kinematic 

measurements of the healthy leg. 

15:15–15:30 ThCT7.3 

Ergonomic considerations for anthropomorphic 

wrist exoskeletons: A simulation study on 

the effects of joint misalignment 

M. Esmaeili, K. Gamage, E. Tan and D. Campolo 

School of Mechanical and Aerospace Engineering 


• We consider a 2dof model with non- 

intersecting joints for both the human wrist 

and the exoskeleton 

• A viscoelastic attachment between the 

human hand and the handle of the exo is 

modeled via 4 non-collinear springs to 

allow kinematics differences between 

human and exoskeleton 

• Discomfort is quantified as the amount of 

potential energy stored in the deformation 

of the viscoelastic attachment 

• From the known distribution of joint offset 

for humans and the simulated discomfort 

function, we compute the optimal joint 

offset for the exoskeleton by solving the 

‘one-size-fits-all’ problem 

2dof kinematic model of the wrist 

and exoskeleton with viscoelastic 

hand-handle attachment 

15:00–15:15 ThCT7.2 

Active Air Mat for Comfortable and Easy to Wear 

a Forearm Support System 

Yasuhisa Hasegawa, Munenori Tayama, Takefumi Saito and 

Yoshiyuki Sankai 

Department of Intelligent Interaction Technologies, University of Tsukuba, 

Japan 

• An active air mat installed in interface 

parts of the exoskeleton improves 

comfortableness and easiness to wear 

an exoskeleton on a forearm. 

• One of the most important effects is to 

minimize constriction of blood flow by 

changing contacting areas with a 

human arm from the periphery to the 

trunk. 

• The active air mat is evaluated from the 

viewpoint of pressure distribution, blood 

flow, wearing time, releasing time, 

body-holding rigidity, and temperature 

and humidity of a user’s skin in resting 

and working states. 


–217– 

Forearm support system and 

components of active air mat 

15:30–15:35 ThCT7.4 

The Development and Testing of a Human 

Machine Interface for a Mobile Medical 

Exoskeleton 

Katherine Strausser and H. Kazerooni 

Mechanical Engineering, University of California, Berkeley, USA 

• The Human Machine Interface (HMI) 

allows a user to control a mobile 

exoskeleton independently. 

• The HMI is implemented on eLEGS, a 

mobile exoskeleton for Spinal Cord Injury 

patients. 

• This method uses natural gestures to 

operate the machine easily and naturally. 

The Human Machine Interface is 

implemented on eLEGS.


Exoskeleton Robots & Gait Rehabilitation 

Chair Bernard Espiau, INRIA 

Co-Chair Constantinos Mavroidis, Northeastern Univ. 

15:35–15:40 ThCT7.5 

A Self-Adjusting Knee Exoskeleton 

for Robot-Assisted Treatment of Knee Injuries 

Mehmet Alper Ergin and Volkan Patoglu 

Faculty of Engineering and Natural Sciences 

Istanbul, Turkey 

• A novel active device for robot-assisted 

rehabilitation that accommodates 

transitional movements of the knee joint 

as well as its rotation 

• 3 Degrees of Freedom device with 2 

translations and one rotation 

• Enables both passive and active 

alignment of the knee joint 

• Impedance controller design 

• Characterization and tracking 

performance of the device are presented 

15:40–15:45 ThCT7.6 

Alterations in Body Weight Shifting during 

Robotic Assisted Gait Rehabilitation 

using NaTUre-gaits 

H. B. Lim, Trieu Phat Luu, K. H. Hoon, Xingda Qu, and K. H. Low 

School of Mechanical and Aerospace Engineering, 


Adela Tow 

Department of Rehabilitation Medicine, Tan Tock Seng Hospital, Singapore 

• Pelvic assistance for robotic assisted gait 


• Investigation of body weight shifting of 

human subject during gait rehabilitation 

with NaTUre-gaits 

• Study of the Center of Pressure (COP) 

trajectory during robotic assisted gait 


• In this study, COP trajectory is found to be 

affected by robotic system 


–218– 

Robotic Arm 

(right) 

Contact surface 

Robotic Arm 

(left) 

Pelvic assistance mechanism


Aerial Robots: Navigation, Tracking & Landing 

Chair Stefan Hrabar, CSIRO ICT Centre 

Co-Chair Mark Minor, Univ. of Utah 

14:45–15:00 ThCT8.1 

Chasing a moving target from a flying UAV 

Céline Teulière and Laurent Eck 

Interactive Robotics Unit, CEA-LIST, France 

Eric Marchand 

Lagadic Unit, Université de Rennes 1, France 

• Robust visual tracking system: 

• Multiple kernel tracking in particle 

filtering framework 

• Initialisation/reinitialisation system 

• Hierarchical control scheme for UAV 

position and yaw control 

• Real-time chasing experiments robust to 

noisy video transmission, some complete 

occlusions, and scale and orientation 

changes. 

UAV 

Internal view 

15:15–15:30 ThCT8.3 

Autonomous Miniature Blimp Navigation 

with Online Motion Planning and Re-planning 

Jörg Müller, Norman Kohler, and Wolfram Burgard 


• Approach to autonomous navigation 

for miniature indoor blimps in mapped 

environments 

• Multi-stage planning algorithm for 

strongly goal-directed tree-based 

kinodynamic motion planning 

• A* path generation in a 4-dimensional 

subspace and path-guided sampling in 

the full 12-dimensional state space 

• Navigation on a round-trip using 

motion capture state estimation and an 

LQR controller 

• Implemented and evaluated on a real 

robotic indoor blimp 

Our indoor blimp navigating 

autonomously through a narrow 

passage between two obstacles. 

15:00–15:15 ThCT8.2 

A Fast and Adaptive Method for Estimating UAV 

Attitude from the Visual Horizon 

Richard J. D. Moore, Saul Thurrowgood, Daniel Bland, 

Dean Soccol & Mandyam V. Srinivasan 

The Queensland Brain Institute, The University of Queensland, Australia 

• A classifier is used to 

segment wide FoV images 

into sky and ground 

regions 

• The classifier is trained 

continuously allowing onthe-fly 

initialisation and the 

ability to adapt to changing 

environments 

• Matching the classified 

images against a precomputed 

database allows 

estimation of aircraft 

attitude to within 1.5 o in 

2ms @ 1GHz 


–219– 

An illustration of the attitude estimation process. The 

input image (a) is classified into sky/ground regions 

(b), and then the row and column averages are 

matched against a pre-computed database (c) to 

retrieve the attitude, which is used to generate a 

training mask (d) for the classified. 

15:30–15:35 ThCT8.4 

Efficient Target Geolocation by 

Highly Uncertain Small Air Vehicles 

Ben Grocholsky, Michael Dille and Stephen Nuske 


• Small UAVs have limited sensing and 

typically exhibit significant heading 

error resulting in poor target 

geolocation accuracy. 

• An estimation method is sought that 

can process complex nonlinear 

uncertainty using lightweight 

computing hardware. 

• We present an efficient parametric 

representation for nonlinear 

uncertainty estimates with significantly 

lower complexity than existing 

methods. 

• Visual target geolocation results are 

demonstrated on a small UAV platform. 

A typical small UAV platform and target 

location observation uncertainty


Aerial Robots: Navigation, Tracking & Landing 

Chair Stefan Hrabar, CSIRO ICT Centre 

Co-Chair Mark Minor, Univ. of Utah 

15:35–15:40 ThCT8.5 

Beyond Visual Range Obstacle Avoidance and 

Infrastructure Inspection by an 

Autonomous Helicopter 

Torsten Merz and Farid Kendoul 

ICT Centre, CSIRO, Australia 

• LIDAR-based perception and guidance 

system for autonomous helicopters 

operating at low altitude in unknown 

environments 

• Efficient methods for terrain following, 

goal-oriented obstacle avoidance, and 

close-range inspection 

• Implemented on board the CSIRO 

unmanned helicopter and flight tested in 

several different mission scenarios 

• Successfully completed 37 inspection 

missions including 2 missions beyond 

visual range without a backup pilot 

15:45–16:00 ThCT8.7 

Reactive Obstacle Avoidance for 

Rotorcraft UAVs 

Stefan Hrabar 

Australian Research Centre for Aerospace Automation (ARCAA) / CSIRO, 

Brisbane, Australia 

• A goal-oriented reactive avoidance 

technique suitable for Rotorcraft UAVs. 

• Uses a 3D occupancy map 

representation of sensed obstacles. 

• Cylindrical Safety Volume is tested for 

obstacles ahead of the UAV. 

• Efficient collision checking within the 

cylinder achieved by approximate 

nearest neighbor search in a Bkd-tree 

representation of occupied voxels. 

• Generates an ‘Escape Point’ waypoint 

providing a path around the obstacles. 

• Performs in real-time using on-board 

processing. 

• Successful obstacle avoidance results 

presented with an Unmanned 

Helicopter 

The CSIRO Unmanned Helicopter fitted 

with stereo and lidar sensors for 

obstacle avoidance. 

15:40–15:45 ThCT8.6 

Coordinated Landing of a Quadrotor on a Skid- 

Steered Ground Vehicle in the Presence of Time 

Delays 

John Daly, Yan Ma, and Steven Waslander 

Department of Mechanical and Mechatronics Engineering, University of 

Waterloo, Canada 

• We present a control technique to 

autonomously land a quadrotor on a 

moving ground vehicle 

• Feedback linearizing controllers are used 

for the quadrotor and ground vehicle 

• A joint controller to stabilize the difference 

in position and the velocity is designed to 

accomplish a rendezvous 

• Time delay stability analysis is performed 

to determine delay margin of closed loop 

system 

• Results verified through numerical 

simulation 

16:00–16:15 ThCT8.8 

Avian-Inspired Passive Perching Mechanism for 

Robotic Rotorcraft 

Courtney E. Doyle, Justin J. Bird, Taylor A. Isom, 

C. Jerald Johnson, Jason C. Kallman, Jason A. Simpson, 

Raymond J. King, Jake J. Abbott, and Mark. A. Minor 

Mechanical Engineering, University of Utah, United States 

• Mechanism inspired by songbirds 

to enable perching on cylindrical 

surfaces. 

• Compliant, underactuated, tendondriven 

gripper provides closure 

around perch. 

• Leg mechanism with compliant 

joints converts rotorcraft weight 

into actuation force for gripper. 


–220– 

Passive mechanism for perching on cylindrical 

surfaces. LEFT: Mechanism in relaxed state; 

CENTER: Mechanism perching on 49mm rod; 

RIGHT: Mechanism perching on 33mm rod.


Swarms and Flocks 

Chair Dylan Shell, Texas A&M Univ. 

Co-Chair Radhika Nagpal, Harvard Univ. 

14:45–15:00 ThCT9.1 

Communication assisted navigation in robotic 

swarms: self-organization and cooperation 

F. Ducatelle, G. A. Di Caro and L. M. Gambardella 

IDSIA, USI/SUPSI, Switzerland 

C. Pinciroli 

IRIDIA, ULB, Belgium 

F. Mondada 

LSRO, EPFL, Switzerland 

• Robots guide each other’s 

navigation by passing wireless 

messages over a range and 

bearing communication system 

• The algorithm supports efficient 

navigation while being robust to 

robot failures 

• Multiple robots moving between 

two targets can self-organize into 

a dynamic chain (see figure) 

• This self-organization supports 

cooperation and improves 

navigation efficiency 

15:15–15:30 ThCT9.3 

The Distributed Co-Evolution 

of an Embodied Simulator and Controller 

for Swarm Robot Behaviours 

Paul O'Dowd, Alan Winfield and Matthew Studley 

Bristol Robotics Laboratory, University of the West of England, UK 

• Each robot evolves a controller solution 

through embodied simulator 

• Real world robot performance metric 

used to indirectly measure environment 

correspondence of the embodied 

simulator 

• Real world robot performance metric 

used to collectively and competitively 

evolve the environment model 

• Swarm of robots can adapt to 

environmental change 

Five processes on board each 

robot to co-evolve both robot 

controller and embodied 

simulator. 

15:00–15:15 ThCT9.2 

Effect of Sensor and Actuator Quality 

on Robot Swarm Algorithm Performance 

Nicholas Hoff, Robert Wood, and Radhika Nagpal 

Computer Science, Harvard University, United States 

• The performance of a swarm depends on 

the hardware quality of the robots. 

• We vary the quality of the sensors and 

actuators, and measure the effect on 

algorithm performance. 

• Large amounts of hardware error are 

required before performance appreciably 

decreases. 


–221– 

Varying number of communication 

receivers. 

15:30–15:35 ThCT9.4 

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Swarms and Flocks 

Chair Dylan Shell, Texas A&M Univ. 

Co-Chair Radhika Nagpal, Harvard Univ. 

15:35–15:40 ThCT9.5 

Gas Source Localization in Indoor Environments 

using Multiple Inexpensive Robots and Stigmergy 

Maurizio Di Rocco 

DIA, Università degli Studi Roma Tre, Italy 

Matteo Reggente, Alessandro Saffiotti 

AASS, Orebro University, Sweden 

• Gas source localization is difficult, e.g., 

because gas concentration does not have a 

smooth gradient due to turbulent diffusion 

• We generate an artificial gradient that 

combines gas concentration and distance, 

and leads to likely location of gas source 

• The gradient is stored in the environment 

(stigmergy) using a grid of RFID tags 

• Through stigmergy, robots only need 

minimal sensing and computing resources 

• Through stigmergy, cooperation among 

multiple robots emerges naturally 

Top: experimental set-up. 

Bottom: computed gas 

concentration map. 

15:45–16:00 ThCT9.7 

Human Swarm Modeling in Exhibition Space 

and Space Design 

Masafumi Okada, Yuichi Motegi and Ko Yamamoto 

Department of Mechanical Sciences and Engineering, 

Tokyo TECH, Japan 

• The purposes of this research are layout 

optimization of exhibits to reduce 

congestion and amenity space design. 

• Human behavior including individual 

characteristics is modeled by twodimensional 

vector field and multidimensional 

extension. 

• A Layout of exhibits is optimized based on 

the proposed model by minimizing 

collision avoidance of individuals. 

• The proposed optimization method is 

verified by simulations and experiments 

using swarm robots. 

The experiment verification using 

swarm robots 

15:40–15:45 ThCT9.6 

Reynolds flocking in reality with fixed-wing robots: 

communication range vs. maximum turning rate 

Sabine Hauert, Severin Leven, Maja Varga, Jean-Christophe 

Zufferey and Dario Floreano 

Laboratory of Intelligent Systems, EPFL, Switerland 

Fabio Ruini, Angelo Cangelosi 

Centre for Robotics and Neural Systems, University of Plymouth, UK 

• Demonstrated Reynolds flocking 

using swarms of 10 flying robots. 

• Results show tradeoff between 

communication range and flight 

dynamics on flocking performance. 

• Developed infrastructure enables 

aerial swarm experiments with a 

single operator. 


–222– 

Swarm of flying robots used to 

perform Reynolds flocking. 

16:00–16:15 ThCT9.8 

ARGoS: a Modular, Multi-Engine Simulator for 

Heterogeneous Swarm Robotics 

Carlo Pinciroli, Vito Trianni, Rehan O’Grady, Giovanni Pini, 

Arne Brutschy, Manuele Brambilla, Nithin Mathews, 

Eliseo Ferrante and Marco Dorigo 

IRIDIA, CoDE, Université Libre de Bruxelles, Belgium 

Gianni di Caro, Frederick Ducatelle and Luca Maria Gambardella 

IDSIA, USI-SUPSI, Switzerland 

Timothy Stirling 

LIS, École Polythechnique Fédérale de Lausanne, Switzerland 

Álvaro Gutiérrez 

ETSI Telecomunicación, Universidad Politécnica de Madrid, Spain 

• A novel, open source multi-robot 

simulator targeted for large 

heterogeneous swarms of robots 

• ARGoS’ modular architecture is 

designed for scalability and 

extensibility 

• A unique feature of ARGoS is that it is 

possible to divide the space into subspaces 

managed by different physics 

engines 

• ARGoS’ architecture is multi-threaded 

and free of race conditions 

Results show that ARGoS can 

simulate 10,000 simple robots 

40% faster than real time

Session ThDT1 Continental Ballroom Thursday, September 29, 2011, 16:45–17:45 

Forum: On Robotics Conferences: A Town Hall Meeting 

Chair Peter Corke, QUT 

Co-Chair 


–223–

Session ThDT3 Continental Parlor 3 Thursday, September 29, 2011, 16:45–17:45 

Marine Systems II 

Chair Ioannis Rekleitis, McGill Univ. 

Co-Chair Shugen Ma, Ritsumeikan Univ. 

16:45–17:00 ThDT3.1 

ePaddle Mechanism: Towards The Development of 

A Versatile Amphibious Locomotion Mechanism 

Yi Sun and Shugen Ma 

Department of Robotics, Ritsumeikan University, Japan 

• An eccentric paddle locomotion mechanism (ePaddle) for amphibious 

tasks is proposed; 

• Three terrestrial and two aquatic gaits are introduced, including wheeled 

rolling, legged walking, hybrid wheeled, rotational paddling and oscillating 

paddling gaits; 

• Forward and inverse kinematic models for above gaits are derived; 

• Prototype design, experiments and simulations are presented to verify 

the idea of ePaddle mechanism. 

17:15–17:30 ThDT3.3 

MARE: Marine Autonomous Robotic Explorer 

Yogesh Girdhar, Anqi Xu, Bir Bikram Dey, Malika Meghjani, 

Florian Shkurti, Ioannis Rekleitis, and Gregory Dudek 

School of Computer Science, McGill University, Canada 

• An autonomous airboat robot suitable for 

exploration-oriented tasks in both closed 

and open water environments 

• A coverage-driven exploration strategy for 

systematically exploring a bounded region 

with arbitrary obstacle based on a 

complete and optimal coverage algorithm 

• A surprise-driven exploration strategy for 

steering the robot on a path that might 

lead to potentially surprising observations 

• A programming tool and accompanied 

software management system for 

respectively specifying and regulating 

concurrent robot behaviors and activities 

Our MARE robot being deployed 

to visually explore a coastal coral 

reef region autonomously 

17:00–17:15 ThDT3.2 

Generic Architecture for Multi-AUV Cooperation 

Based on a Multi-Agent Reactive 

Organizational Approach 

Nicolas Carlési, Fabien Michel, 

Bruno Jouvencel and Jacques Ferber 

LIRMM, Univ. Montpellier 2, France 

• Objective: 

Design an architecture for 

specifying AUV flotillas at a high 

level of abstraction, regardless of 

mission context and AUVs 

characteristics and skills. 

• Proposed approach: 

- A organizational model is used to 

ease and regulate interactions 

between heterogeneous AUVs. 

- A behavioral reactive approach is 

used to limit communication. 

• Simulation: 

Heterogeneous AUVs cooperate 

for exploring and mapping an area. 


–224– 

REMORAS: a three-layer control architecture 

17:30–17:45 ThDT3.4 

State Estimation of an Underwater Robot Using 

Visual and Inertial Information 

F. Shkurti, I. Rekleitis, M. Scaccia, and G. Dudek 

School of Computer Science, McGill University, Canada 

• Single camera, depth sensor, and 

IMU used for underwater robot 

state estimation 

• Challenging lighting conditions 

• Unpredictable motion 

• EKF used to estimate the pose and 

velocities of the vehicle 

• Experimental validation using data 

sets collected over coral reefs 

Aqua swimming over the coral reef


Novel System Designs: Locomotion 

Chair François Pierrot, CNRS - LIRMM 

Co-Chair Pei-Chun Lin, National Taiwan Univ. 

16:45–17:00 ThDT4.1 

Shape and Location Design of Supporting Legs 

for a New Water Strider Robot 

Wu Licheng 1 , Wang Shuhui 2 , Marco Ceccarelli 3 , 

Yuan Haiwen 2 and Yang Guosheng 1 

1 School of Information Engineering, Minzu University of China, China 

2 School of Automation Science & Electrical Engineering, Beihang Univ., China 

3 Laboratory of Robotics and Mechatronics, University of Cassino, Italy 

• Water Strider Robot 

(WSR) stands and moves 

on water by surface 

tension. 

• Shape and location of 

supporting legs for 

enhancing the lift force is 

critical to a WSR 

A photo of a Water Dancer II-a is standing on the water. 

• A supporting leg is approached as Euler-Bernoulli elastic curved beam 

and a method for designing its optimal shape is proposed. 

• A method for properly locating supporting legs on the robot body is 

proposed by analyzing the influence of leg location to lift force and the 

relationship of supporting legs’ with robot’s roll-resistant capability. 

• The proposed methods are used and validated on a developed Water 

Dance II-a as shown in the figure. 

17:15–17:30 ThDT4.3 

HAMR 3 : An Autonomous 1.7g Ambulatory Robot 

Andrew T. Baisch, Michael Karpelson, and Robert J. Wood 

School of Engineering and Applied Sciences, and the Wyss Institute for 

Biologically Inspired Engineering, Harvard University, USA 

Christian Heimlich 

Ecole Polytechnique Fédérale de Lausanne, Switzerland 

• Presentation of a 1.7g, 4.7cm 

hexapod robot as a platform for 

research on centimeter-scale 

walking systems. 

• Results during autonomous 

locomotion. 

• Description of onboard high 

voltage electronics to drive 

piezoelectric actuators. 

The third generation Harvard 

Ambulatory MicroRobot, HAMR 3 

17:00–17:15 ThDT4.2 

Locomotion Approach of REMORA: a REonfigurable 

MObile Robot for manufacturing Applications 

Hai Yang and Cédric Baradat 

Tecnalia, France 

Sébastien Krut and François Pierrot 

LIRMM, CNRS-Université de Montpellier II, France 

• A quadruped uses eight actuators for 

achieving manufacturing and 

locomotion tasks in large workspaces 

• There are lockers on some of the 

passive joints and clamping devices at 

the end of its limbs 

• The locking strategy of the robot is 

formulated as an optimization problem 

• The robot can achieve locomotion with 

respect to some extra constrains in the 

workspaces 

17:30–17:45 ThDT4.4 

Experimental Dynamics of Wing Assisted 

Running for a Bipedal Ornithopter 

Kevin Peterson and Ronald Fearing 

EECS, University of California, Berkeley, USA 

• The wing assisted dynamics of quasi-static 

and dynamic gaits are examined by using an 

onboard accelerometer. 

• We compare accelerations for bipedal 

running with and without wings and find 

flapping wings result in a smoother, faster 

dynamic gait. 

• With wings, peak accelerations are ±22 

m/s 2 and the speed is 1.5 m/s; without, the 

accelerations peak at ±47 m/s 2 with speed 

0.35 m/s. 

• Efficiency of running with and without wing 

assistance measured to be 53.1 J/kg-m and 

89.7 J/kg-m. 


–225– 

The Bipedal Ornithopter for 

Locomotion Transitioning (BOLT) 

is a hybrid flying/crawling robot 

used to study the interactions 

between legs and wings, and 

terrestrial/aerial transitions.


Climbing & Brachiation 

Chair Fumiya Iida, ETH Zurich 

Co-Chair Motoji Yamamoto, Kyushu Univ. 

16:45–17:00 ThDT5.1 

CLASH: Climbing Loose Cloth 

Paul Birkmeyer, Ronald S. Fearing 

Department of Electrical Engineering and Computer Science 

University of California, Berkeley, United States 

Andrew G. Gillies 


University of California, Berkeley, United States 

• 10 cm, 15 gram hexapedal robot capable 

of climbing vertical loose cloth at 15 cm/s 

• Single drive actuator 

• Passive foot design allows easy 

engagement, disengagement of surface 

• Novel design manages body dynamics to 

improve climbing performance 

17:15–17:30 ThDT5.3 

Scaling Walls: 

Applying Dry Adhesives to the Real World 

Elliot W. Hawkes, John Ulmen, Noe Esparza, 

and Mark R. Cutkosky 

Department of Mechanical Engineering, Design Division, 

Stanford University, United States 

• We present two foot mechanisms 

inspired by the structures in gecko 

toes 

• Both allow almost 100% contact even 

with significant initial misalignment 

• The tendon-inspired design with large 

patches achieves adhesion equal to 

small aligned test samples 

• Scaling may allow a robot as large as 

100kg to climb 

Stickybot III climbing a painted 

metal door with a tendon-based 

foot design. 

17:00–17:15 ThDT5.2 

Shaping Energetically Efficient Brachiation 

Motion for a 24-DOF Gorilla Robot 

Stepan Pchelkin 1 Anton Shiriaev 1 Uwe Mettin 1 Leonid Freidovich 2 

1 Norwegian University of Science and Technology, Norway 

2 Umeå University, Sweden 

Tadayoshi Aoyama Zhiguo Lu Toshio Fukuda 

Nagoya University, Japan 

• Problem: Trajectory generation for a 

24-DOF gorilla robot with passive wrist 

• We present a numerically tractable 

procedure with iterative heuristics and 

rigorous tools of analytical mechanics 

to minimize the energetic cost of 

transport 

• (step 1) Blind Numerical Search with 

Different Assumptions on Geometrical 

Constraints 

• (step 2) Relaxed Numerical Search 

with Gradient and Hessian of Cost 

Function 

17:30–17:45 ThDT5.4 

A Climbing Robot Based on Hot Melt Adhesion 

Marc Osswald and Fumiya Iida 

Bio-Inspired Robotics Lab, ETH Zurich, Switzerland 

• Hot Melt Adhesion can provide up to 

150N/cm 2 shear force for climbing robot 

locomotion 

• HMA can be used to climb on different 

materials in the environment 

• Investigation of HMA thermoplasticity and 

controllability for climbing robot locomotion 

• Design and control of a climbing robot 

platform (body size 16cm and weight 

0.6kg) 

• Demonstration and analysis of climbing 

robot locomotion at 0.17m/min. 


–226– 

The Climbing Robot Platform


Novel System Designs: Sensing and Manipulation 

Chair Raj Madhavan, UMD-CP/NIST 

Co-Chair Chang-Soo Han, Hanyang Univ. 

16:45–17:00 ThDT6.1 

Quadrocopter Ball Juggling 

Mark Müller, Sergei Lupashin and Raffaello D’Andrea 

Institute for Dynamic Systems and Control, ETH Zurich, Switzerland 

• Quadrocopters with attached rackets hitting a table 

tennis ball 

• Operating in a 10x10x10m motion capture volume 

• Trajectory generation, ball state estimation 

• Adaptation by estimation of ball’s drag coefficient , 

racket characteristics and aiming bias 

• Three modes of operation: 

• Solo quadrocopter play 

• Quadrocopter/human play 

• Cooperative two quadrocopter play 

17:15–17:30 ThDT6.3 

Compact Design of a Torque Sensor using 

Optical Technique and its Fabrication for 

Wearable and Quadruped Robots 

Sarmad Shams, Dongik Shin, Ji Yeong Lee, Kyoosik Shin and 

Chang-Soo Han 

Dept. of Mechanical Engineering, Hanyang Univ., South Korea 

Jungsoo Han 

Dept. of Mechanical Systems Engineering, Hansung University, South Korea 

• We have developed a compact torque 

sensor for a wearable robot: dimension of 

Ø80�11 mm and range of 100 Nm. 

• The deflection is measured by a photointerrupter 

instead of strain gages. 

• A monolithic flexure mechanism made of 

titanium was designed verified by FEM 

(deflection and stress). 

• The performance such as linearity and 

repeatability was evaluated by 

experiments 

Photo-interrupter-based 

torque sensor 

17:00–17:15 ThDT6.2 

2-DOF Contactless Distributed Manipulation 

Using Superposition of Induced Air Flows 

Anne Delettre, Guillaume J. Laurent and Nadine Le-Fort Piat 

Femto-ST Institute, UFC-ENSMM-UTBM-CNRS, Besançon, France 

• Contactless conveyor 

• Original aerodynamic 

traction principle 

• Induced air flow modeling 

• Two dimensions position 

control and trajectory 

tracking 

17:30–17:45 ThDT6.4 

Development of a High Efficiency and High Reliable 

Glass Cleaning Robot with a Dirt Detect Sensor 

Yoshio Katsuki, Takeshi Ikeda and Motoji Yamamoto 

Faculty of Engineering, Kyushu University, Japan 

• This research proposes a trajectory tracking 

control method for a glass cleaning robot. 

• The robot can follow a desired path for glass 

cleaning with the proposed control method. 

• This research proposes a dirt detect sensor 

for a glass cleaning robot, too. 

• A motion control method to guarantee 

complete cleaning with the dirt detect 

sensor is proposed. 


–227– 

Glass cleaning robot with a dirt detect 

sensor on a partly dirty glass


Medical Robotics: Motion Planning & State Estimation 

Chair Michael Zinn, Univ. of Wisconsin - Madison 

Co-Chair Allison M. Okamura, Stanford Univ. 

16:45–17:00 ThDT7.1 

Modeling Approach for Continuum Robotic 

Manipulators: Effects of Nonlinear Internal 

Device Friction 

Jinwoo Jung, Ryan Penning, Nicola Ferrier, and Michael Zinn, 


University of Wisconsin – Madison, USA 

• A new continuum robotics lumpedparameter 

modeling approach is 

presented 

• Effectively models internal friction from 

sources such as sliding control tendons 

and relative telescoping motion 

• Rresults demonstrate advantage of 

new approach as compared to 

modeling with constant curvature 

assumption 

• Comparison with experimental results 

demonstrates efficacy of approach 

Nonlinear internal friction can 

significantly distort tip trajectory 

17:15–17:30 ThDT7.3 

Motion Planning for Concentric Tube Robots 

Using Mechanics-based Models 

Luis G. Torres and Ron Alterovitz 

Deptartment of Computer Science 

University of North Carolina at Chapel Hill, USA 

• New motion planner for concentric tube 

robots for minimally invasive surgery 

• Uses mechanics-based model of robot 

shape and considers uncertainty in 

actuation and kinematics 

• Minimizes estimated probability of collision 

with anatomical obstacles 

• Extends sampling-based Rapidly- 

Exploring Roadmap with goal bias for fast 

performance 

• Apply to challenging skull base 

neurosurgery procedure 

Concentric tube robot executing 

motion plan to reach pituitary 

gland through nasal cavity 

17:00–17:15 ThDT7.2 

Inequality Constrained Kalman Filtering for the 

Localization and Registration of a Surgical Robot 

Stephen Tully 

Electrical and Computer Engineering, Carnegie Mellon University, USA 

George Kantor and Howie Choset 


• For image-guided surgery, preoperative 

surface models provide geometric 

constraints on the robot path 

• We introduce an uncertainty projection 

method for inequality constrained Kalman 

filtering 

• Nonlinear constraints are handled with a 

pseudo-measurement update process 

• We demonstrate localization and 

registration of the HARP surgical probe 

using constrained filtering 

• Registration parameters are automatically 

updated during image-guidance 

experiments 


–228– 

Correcting snake robot localization 

for image-guided surgery 

17:30–17:45 ThDT7.4 

State Estimation and Feedforward Tremor Suppression 

for a Handheld Micromanipulator with a Kalman Filter 

Brian C. Becker, Robert A. MacLachlan, Cameron N. Riviere 


• Active compensation of tremor in fully 

handheld micrompanipulators is limited by 

latencies in actuation 

• By modeling tremor, more effective tremor 

cancellation is possible by anticipating and 

correcting for future hand motion 

• A Kalman filter estimates state and 

models hand motion, providing tremor 

predictions for a feedforward model 

• Incorporating a feedforward model into the 

control loop of our Micron manipulator 

reduces tremor over 50% compared to the 

existing feedback-only control method 

Micron and experimental setup


Aerial Robots 

Chair Randy Beard, Brigham Young Univ. 

Co-Chair Mac Schwager, Univ. of Pennsylvania 

16:45–17:00 ThDT8.1 

Quadcopter Breaking the MAV Endurance 

Record with Laser Power Beaming 

Michael C. Achtelik, Jan Stumpf, 

Ascending Technologies GmbH, Germany 

Daniel Gurdan and Klaus-Michael Doth 

Ascending Technologies GmbH, Germany 

• Flexible Quadrocopter Design 

• Separate processor for experiments 

• SDK for different control levels 

• MATLAB/Simulink Interface 

• Position Control 

• High update rate of 1 kHz 

• Position estimation based on LED 

tracking and IMU measurements 

• Powered by Laser Power Beaming 

• Small Buffer Battery with Recharge in 

Flight Capability 

• World Endurance Record with a 1 kg UAV: 

• 12h 27min in 2010 in Everett, WA 

Laser powered MAV “AscTec Pelican” 

17:15–17:30 ThDT8.3 

Quadrocopter Performance Benchmarking 

Using Optimal Control 

Robin Ritz, Markus Hehn, 

Sergei Lupashin, and Raffaello D’Andrea 

Institute for Dynamic Systems and Control, ETH Zurich, Switzerland 

• Numerical method for computing 

quadrocopter maneuvers satisfying 

Pontryagin’s minimum principle for timeoptimality. 

• Time-optimal maneuvers are a useful 

benchmark to compare other control 

strategies to. 

• Structure of time-optimal maneuvers is 

shown for various translations in two 

dimensions. 

• Experimental results demonstrate validity 

of computed trajectories. 

Time-Optimal Maneuvers for 

Vertical Displacements 

17:00–17:15 ThDT8.2 

Utilizing an Improved Rotorcraft Dynamic Model 

in State Estimation 

Robert Leishman, Jeff Ferrin, and Timothy McLain 

Mechanical Eng. Dept., Brigham Young University, USA 

John Macdonald, Stephen Quebe, and Randal Beard 

Elec. & Comp. Eng. Dept., Brigham Young University, USA 

• We present an improved dynamic 

model for small multirotor aircraft 

suitable for indoor flight. 

• The model enables more accurate 

IMU-based state estimates. 

• The benefit is reduced need for 

computationally expensive 

exteroceptive sensor updates. 

• The benefit is most helpful to payload 

constrained vehicles such as those 

flying indoors. 


–229– 

This figure compares the truth to two 

estimates of pitch: based on our 

improved model (dotted line) & a more 

traditional approach (dashed line) 

17:30–17:45 ThDT8.4 

A Scalable Information Theoretic 

Approach to Distributed Robot Coordination 

Brian J. Julian* † , Michael Angermann ‡ , 

Mac Schwager* � , and Daniela Rus* 

*CSAIL, MIT, USA 

† Engineering Division, MIT Lincoln Laboratory, USA 

‡ DLR Oberpfaffenhofen, Germany 

� GRASP Laboratory, UPenn, USA 

• Scalable information theoretic 

approach to infer an environment’s 

state by distributively controlling 

robots equipped with sensors. 

• Novel algorithm for approximating 

joint measurement probabilities 

using consensus and sampling. 

• Distributed controller proven to be 

convergent between consensus 

rounds and, under certain 

conditions, locally optimal. 

• Complexity constant w.r.t. number 

of robots, and linear w.r.t. sensor 

measurement and environment 

discretization cells. 

Five quad-rotor flying robots inferring 

the state of an environment


–230–

Author Index 

C = Chair, CC = Co-Chair, O = Organizer, * = interactive presentation (beside the “teaser” in regular sessions) 

A 

Abadie, Joel.............................................................. MoAT1.7 39 

Abbeel, Pieter........................................................... WeAT6 CC 

.................................................................................. WeAT6 O 

.................................................................................. WeBT5.7 2646 

.................................................................................. WeBT6 CC 

.................................................................................. WeBT6 O 

.................................................................................. ThCT6.7 4877 

Abbott, Jake.............................................................. MoBT1 CC 

.................................................................................. MoBT1.5 445 

.................................................................................. TuAPT10.2 * 

.................................................................................. TuBT5.2 1321 

.................................................................................. ThCT8.8 4975 

Abdo, Nichola ........................................................... WeAT5.5 2180 

.................................................................................. WeBPT10.14 * 

Abeles, Peter............................................................ MoBT2.2 475 

Abelmann, Leon ....................................................... MoBT1.1 421 

Abichandani, Pramod ............................................... WeAT8.2 2306 

Abiko, Satoko ........................................................... MoBT6.7 625 

Achar, Supreeth........................................................ MoAT6.3 227 

.................................................................................. WeAT9.1 2352 

Achtelik, Markus W................................................... WeAT6.8 2242 

Achtelik, Michael C................................................... ThDT8.1 5166 

Ackerman, Jeffrey..................................................... MoAT5.5 203 

.................................................................................. MoBPT10.14 * 

Adibi, Mehrad ........................................................... WeAT9.4 2371 

.................................................................................. WeBPT10.25 * 

Adorno, Bruno Vilhena ............................................. WeBT3.5 2545 

.................................................................................. WeCPT10.8 * 

.................................................................................. ThCT1.7 4658 

Agamennoni, Gabriel................................................ MoAT2.7 92 

Agha-mohammadi, Ali-akbar.................................... ThBT3.1 4284 

Aghasadeghi, Navid ................................................. TuBT9.8 1561 

Aghili, Farhad ........................................................... ThAT1.7 3784 

.................................................................................. ThBT1.3 4187 

Aguiar, António......................................................... WeCT6.8 3166 

Ahlberg, Carl............................................................. TuCT2.2 1626 

Ahmad Sharbafi, Maziar........................................... WeBT7.2 2723 

Ahmadi, Mojtaba....................................................... WeAT7.3 2261 

Ahmed, Zubair.......................................................... WeCT3.4 2992 

.................................................................................. WeDPT10.7 * 

Ahn, Bummo............................................................. ThBT7.7 4516 

Akachi, Kazuhiko...................................................... ThBT5.4 4400 

.................................................................................. ThCPT10.13 * 

Akgun, Baris ............................................................. WeBT5.6 2640 

.................................................................................. WeCPT10.15 * 

Alami, Rachid ........................................................... WeCT1 CC 

.................................................................................. WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Albu-Schäffer, Alin.................................................... TuCT6.6 1789 

.................................................................................. WeAPT10.15 * 

.................................................................................. WeCT4.1 3023 

.................................................................................. WeCT7.5 3199 

.................................................................................. WeDT1.5 3367 

.................................................................................. WeDT9.3 3708 

.................................................................................. WeDPT10.20 * 

.................................................................................. ThAPT10.2 * 

.................................................................................. ThBT1.2 4180 

.................................................................................. ThBT1.7 4215 

.................................................................................. ThBT5.7 4420 

Alequin-Ramos, Freddie........................................... MoBT5.8 588 

Alismail, Hatem......................................................... ThAT6.8 4020 

Allan, Jean-Francois................................................. TuCT7.7 1849 


–231– 

Allen, Peter ............................................................... TuAT6 CC 

.................................................................................. TuAT6 O 

.................................................................................. TuBT6.5 1380 

.................................................................................. TuCPT10.14 * 

.................................................................................. WeAT9.7 2390 

Almaddah, Amr......................................................... TuAT1.4 807 

.................................................................................. TuBPT10.1 * 

Almqvist, Håkan........................................................ ThAT8.2 4078 

Alterovitz, Ron .......................................................... WeAT5 C 

.................................................................................. WeAT5 O 

.................................................................................. WeDT5 CC 

.................................................................................. WeDT5 O 

.................................................................................. ThDT7.3 5153 

Althoff, Daniel ........................................................... WeCT5.6 3114 

.................................................................................. WeDPT10.15 * 

Amato, Nancy ........................................................... WeBT5.5 2632 

.................................................................................. WeCPT10.14 * 

.................................................................................. ThBT3 C 

.................................................................................. ThBT3.1 4284 

Ambrose, Robert ...................................................... TuBT6.1 * 

Ames, Aaron............................................................. TuCT5.3 1723 

Amirat, Yacine .......................................................... TuCT5.7 1749 

An, Sang-ik ............................................................... WeBT7.8 2759 

Andersen, Tim .......................................................... TuCT3.5 1687 

Anderson, Chris........................................................ WeAT6.1 * 

Anderson, Ross ........................................................ ThAT4.5 3917 

.................................................................................. ThBPT10.11 * 

Andersson, Sean ...................................................... WeCT5.5 3108 

.................................................................................. WeDPT10.14 * 

Ando, Noriaki ............................................................ TuAT7.5 1042 

.................................................................................. TuBPT10.17 * 

Ando, Teruhisa ......................................................... WeDT8.6 3673 

.................................................................................. ThAPT10.21 * 

Andreff, Nicolas ........................................................ WeBT9.7 2855 

Ang Jr, Marcelo H..................................................... ThAT4.7 3931 

Angeles, Jorge.......................................................... MoAT7.3 280 

Angermann, Michael................................................. ThDT8.4 5187 

Anisi, David A. .......................................................... MoAT6 CC 

.................................................................................. MoAT6.4 235 

.................................................................................. MoBT6 C 

.................................................................................. MoBPT10.16 * 

Annunziata, Salvatore .............................................. ThAT1.6 3776 

.................................................................................. ThBPT10.3 * 

Antonelli, Gianluca.................................................... WeBT8.3 2778 

.................................................................................. WeCT6 CC 

.................................................................................. WeCT6 O 

.................................................................................. WeCT6.8 3166 

.................................................................................. WeDT6 O 

Aoi, Shinya ............................................................... WeAT7 CC 

.................................................................................. WeAT7.5 2274 

.................................................................................. WeAT7.6 2280 

.................................................................................. WeBPT10.20 * 

.................................................................................. WeBPT10.21 * 

Aoki, Keiji.................................................................. ThAT8.7 4109 

Aoki, Kengo .............................................................. ThCT2.7 4710 

Aoki, Takeshi ............................................................ ThBT8.4 4550 

.................................................................................. ThCPT10.22 * 

Aoyama, Tadayoshi.................................................. ThDT5.2 5094 

Apker, Thomas ......................................................... MoAT3.5 125 

.................................................................................. MoBPT10.8 * 

Arabagi, Veaceslav................................................... ThAT4.8 3937 

Arafuka, Kazushi ...................................................... MoAT9.5 395 

.................................................................................. MoBPT10.26 *

Arai, Fumihito ........................................................... MoAT1 CC 

.................................................................................. MoAT1.3 13 

.................................................................................. MoBT1.4 439 

.................................................................................. TuAPT10.1 * 

.................................................................................. TuBT3.7 1309 

.................................................................................. TuCT3.6 1693 

Arai, Tamio ............................................................... ThCT2.5 4698 

.................................................................................. ThDPT10.5 * 

Arai, Tatsuo .............................................................. MoAT1.5 25 

.................................................................................. MoBT1 C 

.................................................................................. MoBT1.2 427 

.................................................................................. MoBPT10.2 * 

.................................................................................. TuAT1.4 807 

.................................................................................. TuBPT10.1 * 

Araki, Takahiro ......................................................... TuCT9.6 1946 

.................................................................................. WeAPT10.24 * 

Araki, Takaya............................................................ TuBT9.5 1540 

.................................................................................. TuCPT10.23 * 

Arata, Jumpei ........................................................... WeAT4.6 2145 

.................................................................................. WeBPT10.12 * 

Ardö, Håkan.............................................................. WeCT3.1 2971 

Arechavaleta, Gustavo ............................................. TuCT1.8 1612 

Arevalo, Juan Carlos ................................................ TuBT8.8 1507 

Argentieri, Sylvain..................................................... MoAT3.7 137 

Argiles, Alberto ......................................................... ThBT6.5 4448 

.................................................................................. ThCPT10.17 * 

Ariizumi, Ryo ............................................................ TuCT8.8 1907 

Arimoto, Suguru........................................................ TuCT6.1 * 

Armesto, Leopoldo ................................................... ThBT3.8 4335 

Arras, Kai Oliver ....................................................... WeAT1 C 

.................................................................................. WeAT1.1 1968 

.................................................................................. ThAT2 CC 

.................................................................................. ThAT2.6 3838 

.................................................................................. ThAT2.7 3844 

.................................................................................. ThBPT10.6 * 

Arrichiello, Filippo ..................................................... WeBT8 CC 

.................................................................................. WeBT8.3 2778 

.................................................................................. WeCT6.8 3166 

Arslan, Oktay............................................................ WeDT5.2 3507 

.................................................................................. WeDT5.6 3533 

.................................................................................. ThAPT10.12 * 

Artigas, Jordi............................................................. MoAT4.7 177 

.................................................................................. MoBT7.5 665 

.................................................................................. TuAPT10.17 * 

Asada, Harry............................................................. ThAT4 CC 

.................................................................................. ThAT4 O 

.................................................................................. ThAT4.7 3931 

.................................................................................. ThBT4 O 

Asadian, Ali............................................................... WeBT3.6 2551 

.................................................................................. WeCPT10.9 * 

Asama, Hajime ......................................................... WeCT7.6 3207 

.................................................................................. WeDPT10.21 * 

Asano, Fumihiko....................................................... WeAT7.1 2249 

.................................................................................. WeBT7.6 2747 

.................................................................................. WeCPT10.21 * 

Asano, Futoshi.......................................................... MoAT3.8 143 

Asatani, Minami........................................................ ThBT6.7 4463 

Asfour, Tamim .......................................................... TuCT6.2 1761 

.................................................................................. TuCT6.5 1781 

.................................................................................. WeAPT10.14 * 

.................................................................................. ThBT5 C 

.................................................................................. ThBT5 O 

.................................................................................. ThCT5 O 

.................................................................................. ThCT5.1 * 

Asplund, Lars............................................................ TuCT2.2 1626 

Au, Tsz-Chiu............................................................. ThBT9.3 4581 

Aukes, Daniel ........................................................... TuBT6.4 1373 

.................................................................................. TuCPT10.13 * 

Avci, Ebubekir........................................................... MoBT1.2 427 

Ayanian, Nora........................................................... WeCT5.8 3126 


–232– 

Ayusawa, Ko............................................................. WeDT9.2 3701 

Azevedo Coste, Christine ......................................... TuCT5.4 1731 

.................................................................................. WeAPT10.10 * 

Azimi, Ali................................................................... MoAT7.3 

B 

280 

Babic, Jan................................................................. ThCT5.6 4832 

.................................................................................. ThDPT10.15 * 

Babuska, Robert....................................................... WeBT7.3 2729 

Bachmann, Richard J. .............................................. MoAT5.6 209 

.................................................................................. MoBPT10.15 * 

Badino, Hernan......................................................... WeCT9.1 3283 

Baek, Stanley ........................................................... WeBT6.3 2674 

Bagutti, Lorenzo ....................................................... WeBT4.2 2578 

Bahls, Thomas.......................................................... TuAT7.7 1055 

.................................................................................. TuCT9.4 1933 

.................................................................................. WeAPT10.22 * 

Bahr, Fred................................................................. WeCT6.3 3132 

Bailey, Tim................................................................ TuAT2.8 886 

.................................................................................. ThBT9.6 4601 

.................................................................................. ThCPT10.27 * 

Baisch, Andrew......................................................... ThDT4.3 5073 

Bajcsy, Ruzena......................................................... WePL C 

Bajo, Andrea............................................................. WeAT9.7 2390 

Balaguer, Benjamin .................................................. TuBT7.1 1405 

Balasubramanian, Ravi ............................................ TuCT7 CC 

.................................................................................. TuCT7.3 1823 

Balkcom, Devin......................................................... ThBT3.6 4321 

.................................................................................. ThCPT10.9 * 

Ballard, Dana............................................................ TuBT1.4 1194 

.................................................................................. TuCPT10.1 * 

Banno, Yoshihisa...................................................... WeBT7.4 2735 

.................................................................................. WeCPT10.19 * 

Baradat, Cédric......................................................... ThDT4.2 5067 

Barfoot, Timothy ....................................................... MoBT6.8 631 

Barkby, Stephen ....................................................... TuBT2.3 1242 

.................................................................................. ThCT3 C 

Barrows, Geoffrey..................................................... TuAT8.6 1099 

.................................................................................. TuBPT10.21 * 

Bascetta, Luca.......................................................... WeCT3.1 2971 

Basoeki, Fransiska ................................................... WeDT3.6 3480 

.................................................................................. ThAPT10.9 * 

Bastos-Filho, Teodiano............................................. ThAT1.1 3746 

Bates, Terry .............................................................. WeAT9.1 2352 

Baum, Marcus .......................................................... WeAT2.5 2050 

.................................................................................. WeBPT10.5 * 

Bäuml, Berthold ........................................................ TuCT1.4 1585 

.................................................................................. WeAPT10.1 * 

Bauzano, Enrique ..................................................... WeAT3.8 2121 

Bays, Matthew .......................................................... WeCT8.1 3227 

Beard, Randy............................................................ WeBT6.5 2688 

.................................................................................. WeCPT10.17 * 

.................................................................................. ThDT8 C 

.................................................................................. ThDT8.2 5173 

Becattini, Gabriele .................................................... TuBT5.8 1359 

Becker, Aaron........................................................... TuAT9.7 1160 

Becker, Brian C. ....................................................... ThDT7.4 5160 

Beetz, Michael .......................................................... WeCT7.1 3172 

.................................................................................. ThAT9 C 

.................................................................................. ThAT9.4 4141 

.................................................................................. ThBT2.6 4263 

.................................................................................. ThBPT10.25 * 

.................................................................................. ThCPT10.6 * 

Behar, Evan.............................................................. TuCT1.2 1573 

Behera, Laxmidhar ................................................... ThCT2.8 4716 

Behkam, Bahareh..................................................... ThAT4.8 3937 

Bekiroglu, Yasemin................................................... TuBT9.7 1554 

Bekris, Kostas E. ...................................................... WeBT5 C 

.................................................................................. WeBT5 O 

.................................................................................. WeCT8.2 3235 

.................................................................................. WeCT8.7 3268

.................................................................................. ThBT3.2 4292 

Bellens, Steven......................................................... WeCT4.2 3031 

Bellingham, James G. .............................................. WeCT6.3 3132 

Belo, Felipe............................................................... ThAT1.5 3770 

.................................................................................. ThBPT10.2 * 

Belta, Calin ............................................................... WeCT5 CC 

.................................................................................. WeCT5 O 

.................................................................................. WeCT5.2 3087 

.................................................................................. WeCT5.5 3108 

.................................................................................. WeDPT10.14 * 

Ben Amar, Faiz......................................................... MoAT7.2 274 

Bender, John ............................................................ MoAT5.7 215 

Bengel, Matthias....................................................... MoAT6.5 241 

.................................................................................. MoBPT10.17 * 

Benhabib, Beno........................................................ ThBT8.3 4544 

Bennett, Bradford ..................................................... ThAT5.7 3969 

Bennewitz, Maren..................................................... ThCT5.8 4844 

Benson, Hande......................................................... WeAT8.2 2306 

Beranek, Richard...................................................... WeAT7.3 2261 

Bergbreiter, Sarah .................................................... TuCT3.4 1680 

.................................................................................. WeAPT10.7 * 

Bergström, Niklas ..................................................... TuAT1.7 827 

.................................................................................. WeAT2.2 2028 

Berkley, Jeff.............................................................. WeBT3.4 2539 

.................................................................................. WeCPT10.7 * 

Berman, Spring......................................................... WeCT8 CC 

.................................................................................. ThAT4.6 3923 

.................................................................................. ThBPT10.12 * 

Bernardes, Mariana Costa........................................ WeBT3.5 2545 

.................................................................................. WeCPT10.8 * 

Bernardino, Alexandre.............................................. ThCT5.5 4826 

.................................................................................. ThDPT10.14 * 

Bernstein, Matthew................................................... TuCT6.8 1804 

Berret, Bastien.......................................................... ThBT4.3 4354 

Bersch, Christian ...................................................... TuBT7.2 1413 

Berthoz, Alain ........................................................... WePL.1 * 

Bessho, Fumihiro...................................................... TuBT1.7 1214 

Bewley, Thomas....................................................... WeBT7.5 2741 

.................................................................................. WeCPT10.20 * 

.................................................................................. ThAT8.5 4097 

.................................................................................. ThBPT10.23 * 

Beyeler, Felix............................................................ TuAT3.7 919 

Bhatawadekar, Vineet .............................................. TuAT9.8 1167 

Bhattacharyya, Samrat............................................. TuBT6.5 1380 

.................................................................................. TuCPT10.14 * 

Bhoite, Mohit............................................................. ThCT4.7 4797 

Biagiotti, Luigi ........................................................... ThAT3 CC 

.................................................................................. ThAT3.8 3899 

Bialkowski, Joshua J ................................................ WeDT5.3 3513 

Bicchi, Antonio.......................................................... WeBT9.1 2817 

.................................................................................. ThAT1.5 3770 

.................................................................................. ThBPT10.2 * 

Bidaud, Philippe........................................................ WeBT7.7 2753 

Bierbaum, Alexander................................................ TuCT6.2 1761 

Biggs, Geoffrey......................................................... TuAT7 C 

.................................................................................. TuAT7.5 1042 

.................................................................................. TuBPT10.17 * 

Billard, Aude ............................................................. MoBT8.4 710 

.................................................................................. TuAPT10.19 * 

Bin Mohd Shariff, Ahmed Shafeeq ........................... WeDT6.3 3551 

Binney, Jonathan...................................................... WeCT6.5 3147 

.................................................................................. WeDPT10.17 * 

Birbach, Oliver.......................................................... WeCT9 CC 

.................................................................................. WeCT9.4 3305 

.................................................................................. WeDT2.6 3426 

.................................................................................. WeDPT10.25 * 

.................................................................................. ThAPT10.6 * 

Birchfield, Stan ......................................................... ThCT6.6 4871 

.................................................................................. ThDPT10.18 * 

Bird, Justin................................................................ ThCT8.8 4975 


–233– 

Birkmeyer, Paul ........................................................ ThDT5.1 5087 

Bischof, Horst ........................................................... ThAT6.4 3990 

.................................................................................. ThBPT10.16 * 

Biswas, Joydeep....................................................... MoAT2.4 73 

.................................................................................. MoBPT10.4 * 

Bjerkeng, Magnus..................................................... MoAT6.6 247 

.................................................................................. MoBPT10.18 * 

.................................................................................. ThCT2.2 4676 

Björkman, Mårten ..................................................... TuAT1.7 827 

Black, Richard J........................................................ WeBT3.8 2564 

Bland, Daniel Peter................................................... ThCT8.2 4935 

Blanke, Olaf .............................................................. ThCT1.8 4664 

Bleuler, Hannes ........................................................ ThCT1.8 4664 

Bley, Andreas ........................................................... WeBT1.5 2430 

Blodow, Nico............................................................. ThBT2.6 4263 

.................................................................................. ThCPT10.6 * 

Blumel, Marcus......................................................... MoAT5.7 215 

Bó, Antônio Padilha Lanari ....................................... ThCT1.7 4658 

Bo, Liefeng ............................................................... TuAT1.6 821 

.................................................................................. TuBPT10.3 * 

Bobadilla, Leonardo.................................................. WeCT5.4 3101 

.................................................................................. WeDPT10.13 * 

Bocsi, Botond ........................................................... MoBT8.2 698 

Bohg, Jeannette ....................................................... WeDT1.1 3342 

Bok, Yunsu ............................................................... ThBT6.3 4436 

Bolopion, Aude ......................................................... TuAT3.3 894 

Bonani, Michael ........................................................ ThCT4.2 4762 

Bonato, Paolo ........................................................... ThCT7.1 4893 

Book, Wayne ............................................................ ThBT8.5 4556 

.................................................................................. ThCPT10.23 * 

Bordignon, Mirko ...................................................... WeDT8.4 3659 

.................................................................................. ThAPT10.19 * 

Borges, Geovany Araujo .......................................... WeBT3.5 2545 

.................................................................................. WeCPT10.8 * 

Borst, Christoph........................................................ TuCT6.3 1768 

Bosse, Michael ......................................................... ThAT2.5 3830 

.................................................................................. ThBPT10.5 * 

Bouabdallah, Samir .................................................. WeAT6.5 2223 

.................................................................................. WeBPT10.17 * 

Boucher, Jean-David ................................................ WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Boukallel, Mehdi ....................................................... TuAT3.6 913 

.................................................................................. TuBPT10.9 * 

.................................................................................. WeBT2.7 2505 

Boularias, Abdeslam................................................. TuBT9.6 1548 

.................................................................................. TuCPT10.24 * 

Bouraine, Sara.......................................................... WeCT3.3 2985 

Bourdin, Christophe.................................................. TuBT5.4 1333 

.................................................................................. TuCPT10.10 * 

Bouyarmane, Karim.................................................. ThBT5.6 4414 

.................................................................................. ThCPT10.15 * 

Boxerbaum, Alexander ............................................. MoAT5.4 197 

.................................................................................. MoBPT10.13 * 

Boyer, Frédéric ......................................................... TuCT8.7 1901 

Bozorg Grayeli, Alexis .............................................. WeBT3.3 2532 

Braman, Karen ......................................................... WeCT2.2 2928 

Brambilla, Manuele................................................... ThCT9.8 5027 

Brand, Christoph Norbert.......................................... WeCT5.6 3114 

.................................................................................. WeDPT10.15 * 

Branicky, Michael ..................................................... WeBT5.3 2620 

Branson, David ......................................................... TuAT8.5 1093 

.................................................................................. TuBPT10.20 * 

.................................................................................. ThAT7.5 4054 

.................................................................................. ThBPT10.20 * 

Bravo, Ignacio........................................................... ThAT1.1 3746 

Breitenmoser, Andreas............................................. MoAT2.5 79 

.................................................................................. MoBPT10.5 * 

Brethe, Jean-François .............................................. ThAT7 CC 

.................................................................................. ThAT7.7 4066 

Bretl, Timothy ........................................................... TuAT9 CC

.................................................................................. TuAT9.7 1160 

.................................................................................. TuBT9 CC 

.................................................................................. TuBT9.8 1561 

Brewer, Reuben........................................................ WeBT4.1 2570 

Bringout, Gael........................................................... TuBT3.3 1285 

Briot, Sébastien ........................................................ WeDT9.6 3728 

.................................................................................. ThAPT10.24 * 

Broeders, Ivo A. M. J................................................ TuAT5.3 937 

Broggi, Alberto.......................................................... TuCT1.6 1599 

.................................................................................. WeAPT10.3 * 

Brooks, David ........................................................... TuAT8.6 1099 

.................................................................................. TuBPT10.21 * 

Browning, Brett......................................................... ThAT6.8 4020 

Bruneau, Olivier........................................................ ThAT5.4 3951 

.................................................................................. ThBPT10.13 * 

Brunner, Christopher Joseph.................................... WeBT2.5 2489 

.................................................................................. WeCPT10.5 * 

Brutschy, Arne.......................................................... ThCT9.8 5027 

Bruyninckx, Herman ................................................. WeCT3.1 2971 

.................................................................................. WeCT4.2 3031 

.................................................................................. ThCT2.3 4684 

Bryson, Mitch............................................................ ThBT2 CC 

.................................................................................. ThBT2.5 4256 

.................................................................................. ThCPT10.5 * 

Bubeck, Alexander ................................................... MoAT6.5 241 

.................................................................................. MoBPT10.17 * 

Buchaillot, Lionel ...................................................... MoAT1.8 45 

Buchan, Austin ......................................................... TuAT8.2 1075 

Buehler, Martin ......................................................... WeBT7 CC 

Buelthoff, Heinrich H................................................. MoAT4.5 163 

.................................................................................. MoBPT10.11 * 

.................................................................................. WeAT6.4 2215 

.................................................................................. WeBPT10.16 * 

.................................................................................. WePL.1 * 

.................................................................................. WeCT4.3 3039 

Buether, John ........................................................... MoAT4.6 171 

.................................................................................. MoBPT10.12 * 

Buffinton, Keith ......................................................... MoAT5.3 190 

Bugajska, Magdalena............................................... MoAT3.5 125 

.................................................................................. MoBPT10.8 * 

Burch, Derek............................................................. ThBT8.2 4536 

Burdet, Etienne......................................................... WeBT4.2 2578 

.................................................................................. ThAT9.1 4121 

Burgard, Wolfram ..................................................... MoAT2.6 86 

.................................................................................. MoBPT10.6 * 

.................................................................................. TuBT2 C 

.................................................................................. TuBT2.4 1249 

.................................................................................. TuCPT10.4 * 

.................................................................................. WeAT5.5 2180 

.................................................................................. WeBPT10.14 * 

.................................................................................. WeDT9.4 3716 

.................................................................................. ThAPT10.22 * 

.................................................................................. ThBT2.4 4249 

.................................................................................. ThCT8.3 4941 

.................................................................................. ThCPT10.4 * 

Burgess, Stuart......................................................... ThAT7.3 4042 

Burgner, Jessica....................................................... WeBT3.1 2517 

Burguera, Antoni....................................................... WeDT6.7 3577 

Burschka, Darius ...................................................... TuBT1.8 1221 

.................................................................................. WeCT9.3 3297 

Burton, Lisa .............................................................. ThAT3.7 3893 

Buss, Martin.............................................................. WeCT5.6 3114 

.................................................................................. WeDPT10.15 * 

Butzke, Jonathan...................................................... WeCT8.5 3254 

.................................................................................. WeDPT10.23 * 

Buys, Koen ............................................................... WeCT4.2 3031 

Buzzoni, Michele ...................................................... TuCT1.6 1599 

.................................................................................. WeAPT10.3 

C 

* 

Caccavale, Fabrizio.................................................. WeBT8.3 2778 


–234– 

.................................................................................. ThBT1.4 4194 

.................................................................................. ThCPT10.1 * 

Cadeddu, Jeffrey A................................................... WeAT9.4 2371 

.................................................................................. WeBPT10.25 * 

Cai, Binghuang ......................................................... WeDT8.8 3686 

Cai, Chuanwu ........................................................... TuBT8.3 1473 

Cakmak, Maya.......................................................... WeAT1.4 1986 

.................................................................................. WeBPT10.1 * 

Caldwell, Darwin G. .................................................. MoAT8.1 318 

.................................................................................. MoAT9.2 378 

.................................................................................. MoAT9.4 390 

.................................................................................. MoBPT10.25 * 

.................................................................................. TuAT8.5 1093 

.................................................................................. TuBT5.8 1359 

.................................................................................. TuBPT10.20 * 

.................................................................................. WeCT4.4 3047 

.................................................................................. WeDT2.4 3413 

.................................................................................. WeDPT10.10 * 

.................................................................................. ThAT7.5 4054 

.................................................................................. ThAPT10.4 * 

.................................................................................. ThBPT10.20 * 

Calinon, Sylvain........................................................ MoAT8.1 318 

.................................................................................. WeCT4.4 3047 

.................................................................................. WeDT2 C 

.................................................................................. WeDT2.4 3413 

.................................................................................. WeDPT10.10 * 

.................................................................................. ThAPT10.4 * 

Calli, Berk ................................................................. TuAT6.6 995 

.................................................................................. TuBPT10.15 * 

Camilli, Richard ........................................................ MoAT6.8 261 

.................................................................................. ThCT3.2 4722 

Campbell, Mark ........................................................ TuCT1.7 1605 

Campolo, Domenico ................................................. ThCT7.3 4905 

Campos, Mario F. Montenegro................................. WeAT1 CC 

.................................................................................. WeAT1.2 1974 

Cangelosi, Angelo .................................................... ThCT9.6 5015 

.................................................................................. ThDPT10.27 * 

Cannata, Giorgio ...................................................... WeDT9.1 3694 

Cappelleri, David ...................................................... TuAT3 CC 

.................................................................................. TuAT3 O 

.................................................................................. TuAT3.8 925 

.................................................................................. TuBT3 C 

.................................................................................. TuBT3 O 

.................................................................................. TuCT3 CC 

.................................................................................. TuCT3 O 

Carlési, Nicolas......................................................... ThDT3.2 5041 

Carlevaris-Bianco, Nicholas ..................................... ThBT4.7 4378 

Carlson, Rolf............................................................. WeDT1.1 3342 

Caro, Stéphane ........................................................ ThAT7.2 4034 

Carpin, Stefano......................................................... TuBT7.1 1405 

.................................................................................. ThBT8.2 4536 

Carryon, Gabe .......................................................... MoBT5.7 580 

Castellanos, Jose A.................................................. TuBT2.5 1256 

.................................................................................. TuCPT10.5 * 

Castellini, Claudio..................................................... MoBT7.6 672 

.................................................................................. TuAPT10.18 * 

.................................................................................. WeAT3.6 2108 

.................................................................................. WeBPT10.9 * 

Castillo, Pedro .......................................................... WeBT6.4 2682 

.................................................................................. WeCPT10.16 * 

Castro, Sebastian ..................................................... WeCT5.7 3120 

Catoire, Laurent........................................................ ThBT7.1 4477 

Cauli, Nino ................................................................ ThCT5.5 4826 

.................................................................................. ThDPT10.14 * 

Cavusoglu, M. Cenk ................................................. MoAT4 CC 

.................................................................................. MoAT4 O 

.................................................................................. WeDT3 C 

.................................................................................. WeDT3.3 3460 

Ceccarelli, Marco...................................................... ThDT4.1 5061 

Célérier, Charlotte .................................................... WeBT3.3 2532

Censi, Andrea........................................................... WeAT2.6 2056 

.................................................................................. WeBPT10.6 * 

.................................................................................. ThBT6.8 4469 

Chablat, Damien....................................................... TuBT7.8 1453 

.................................................................................. ThAT7.2 4034 

Chae, Hansang......................................................... TuBT7.7 1447 

Chae, Yongwook ...................................................... MoBT7.8 685 

Chaimowicz, Luiz...................................................... ThBT4.6 4372 

.................................................................................. ThCPT10.12 * 

Chakravorty, Suman................................................. ThBT3.1 4284 

Chalon, Maxime........................................................ TuBT6.3 1366 

.................................................................................. ThBT1.7 4215 

Chamberlain, Lyle..................................................... MoAT6.3 227 

Chambers, Andrew................................................... MoAT6.3 227 

Chan, Ambrose......................................................... WeBT9.2 2825 

Chang, Bo................................................................. TuAT3.5 907 

.................................................................................. TuBPT10.8 * 

Chang, Doyoung....................................................... TuAT5.4 943 

.................................................................................. TuBPT10.10 * 

Chang-Siu, Evan ...................................................... TuCT8.5 1887 

.................................................................................. WeAPT10.20 * 

.................................................................................. WeBT2.3 2474 

Chao, Yi.................................................................... WeCT6.4 3140 

.................................................................................. WeDPT10.16 * 

Chapuis, Dominique ................................................. WeCT4.8 3074 

Charpillet, Francois................................................... ThAT8.6 4103 

.................................................................................. ThBPT10.24 * 

Charusta, Krzysztof Andrzej..................................... TuCT6.7 1797 

Chaumette, Francois ................................................ MoBT9.6 768 

.................................................................................. TuAPT10.24 * 

.................................................................................. TuCT1.5 1593 

.................................................................................. WeAPT10.2 * 

.................................................................................. WeBT9 C 

.................................................................................. WeBT9.5 2843 

.................................................................................. WeBT9.6 2849 

.................................................................................. WeCPT10.26 * 

.................................................................................. WeCPT10.27 * 

Chen, Bor-rong ......................................................... ThBT7.3 4488 

Chen, Fei.................................................................. ThCT2.1 4670 

Chen, Haoyao........................................................... MoBT1.6 451 

.................................................................................. TuAPT10.3 * 

Chen, I-Ming............................................................. TuCT7 C 

.................................................................................. WeCT2.3 2935 

Chen, S.Y. ................................................................ TuAT1.3 801 

Chen, Weidong......................................................... WeCT2.7 2959 

Chen, Weihai............................................................ MoBT9.2 744 

Chen, Yi-Jie .............................................................. TuCT9.8 1962 

Cheng, Bo................................................................. MoBT5.6 574 

.................................................................................. TuAPT10.12 * 

Cherubini, Andrea..................................................... TuCT1.5 1593 

.................................................................................. WeAPT10.2 * 

Chiba, Ryosuke ........................................................ ThCT2.5 4698 

.................................................................................. ThDPT10.5 * 

Chiel, Hillel................................................................ MoAT5.4 197 

.................................................................................. MoBPT10.13 * 

Chien, Steve............................................................. WeCT6.4 3140 

.................................................................................. WeDPT10.16 * 

Chilian, Annett .......................................................... WeBT2.6 2497 

Chinzei, Kiyoyuki ...................................................... WeBT4.3 2584 

Chipalkatty, Rahul .................................................... ThBT8.5 4556 

.................................................................................. ThCPT10.23 * 

Chirikjian, Gregory.................................................... ThAT4 O 

.................................................................................. ThBT4 O 

Chitre, Mandar.......................................................... WeDT6 CC 

Chitta, Sachin ........................................................... WeDT2 CC 

Chizeck, Howard ...................................................... WeBT4.8 2614 

Chli, Margarita .......................................................... WeAT6.8 2242 

Cho, Changhyun....................................................... TuCT7.8 1857 

Choi, Dong - Geol..................................................... ThBT6.3 4436 

Choi, Dongmin.......................................................... TuBT7.7 1447 


–235– 

Choi, Hyouk Ryeol.................................................... TuBT7.7 1447 

Chopra, Nikhil ........................................................... MoBT7 C 

.................................................................................. MoBT7.7 679 

.................................................................................. WeAT8.4 2321 

.................................................................................. WeBPT10.22 * 

Choset, Howie .......................................................... MoAT5.8 221 

.................................................................................. MoAT8.7 357 

.................................................................................. MoBT2.3 482 

.................................................................................. TuAT8 CC 

.................................................................................. TuAT8.1 1069 

.................................................................................. TuAT8.2 1075 

.................................................................................. TuBT5.7 1353 

.................................................................................. WeCT8.6 3260 

.................................................................................. WeDPT10.24 * 

.................................................................................. ThAT3.7 3893 

.................................................................................. ThDT7.2 5147 

Choti, Michael........................................................... WeBT3.4 2539 

Chou, Ya-Cheng....................................................... TuBT8.6 1493 

.................................................................................. TuCPT10.21 * 

Christensen, Anders Lyhne ...................................... ThCT4.2 4762 

Christensen, Henrik Iskov......................................... TuBT2.6 1264 

.................................................................................. TuCPT10.6 * 

.................................................................................. ThPT11 C 

Christensen, Kim Hardam ........................................ TuAT2.2 847 

Christensen, Quinton................................................ WeBT4.5 2596 

.................................................................................. WeCPT10.11 * 

Christopher, Ray....................................................... WeDT7.4 3608 

Chuang, Lewis L....................................................... WeCT4.3 3039 

Chung, Timothy H..................................................... ThBT8 C 

.................................................................................. ThBT8.2 4536 

Chung, Wan Kyun .................................................... WeBT3 C 

.................................................................................. WeBT3.2 2524 

Churaman, Wayne.................................................... TuCT3.4 1680 

.................................................................................. WeAPT10.7 * 

Ciliberto, Carlo.......................................................... TuBT9.3 1526 

.................................................................................. ThAT9.6 4154 

.................................................................................. ThBPT10.27 * 

Ciocarlie, Matei......................................................... TuAT6 C 

.................................................................................. TuAT6 O 

Civera, Javier............................................................ TuBT2 CC 

.................................................................................. TuBT2.8 1277 

.................................................................................. ThBT6.5 4448 

.................................................................................. ThCPT10.17 * 

Claassens, Jonathan ................................................ WeAT1.3 1980 

Clark, James Romney .............................................. TuBT5.2 1321 

Cognetti, Marco ........................................................ MoBT2.1 469 

Colas, Francis........................................................... ThAT2.4 3824 

.................................................................................. ThBPT10.4 * 

Coleman, Sonya ....................................................... ThCT2.8 4716 

Colgate, Edward ....................................................... WeAT4.1 * 

Coltin, Brian .............................................................. MoAT2.4 73 

.................................................................................. MoBPT10.4 * 

Comport, Andrew Ian................................................ ThBT2.3 4242 

Conradt, Jorg............................................................ MoBT8 CC 

Conti, Francois ......................................................... WeCT4.1 3023 

Cook IV, Atlas F........................................................ WeDT5.5 3526 

.................................................................................. ThAPT10.11 * 

Cordes, Florian ......................................................... WeDT8.3 3653 

Corke, Peter ............................................................. ThAT3.3 3868 

.................................................................................. ThBT6.1 * 

Correll, Nikolaus ....................................................... ThCT4 C 

.................................................................................. ThCT4 O 

.................................................................................. ThCT4.1 * 

.................................................................................. ThCT4.5 4783 

.................................................................................. ThDPT10.11 * 

Corso, Jason ............................................................ TuCT2.3 1632 

Cortez, Andres.......................................................... WeAT8.6 2333 

.................................................................................. WeBPT10.24 * 

Cosgun, Akansel ...................................................... ThCT1.2 4627 

Costa, Lino ............................................................... WeAT7.7 2286

Coste, Michel............................................................ TuBT7.8 1453 

Cotin, Stephane........................................................ WeBT4.7 2608 

Coutard, Laurent....................................................... WeBT9.5 2843 

.................................................................................. WeCPT10.26 * 

Cowlagi, Raghvendra ............................................... WeDT5.1 3501 

Cowley, Anthony....................................................... TuAT7.6 1048 

.................................................................................. TuBPT10.18 * 

.................................................................................. ThCT4.4 4776 

.................................................................................. ThDPT10.10 * 

Creed, Ross.............................................................. ThAT2.2 3808 

Croft, Elizabeth......................................................... WeAT1.5 1994 

.................................................................................. WeBT9.2 2825 

.................................................................................. WeBPT10.2 * 

.................................................................................. WeDT1 CC 

.................................................................................. WeDT1.4 3361 

.................................................................................. ThAPT10.1 * 

Csató, Lehel ............................................................. MoBT8.2 698 

Cutkosky, Mark......................................................... MoBT9.7 774 

.................................................................................. TuAT6.1 * 

.................................................................................. TuBT6.4 1373 

.................................................................................. TuCPT10.13 * 

.................................................................................. WeBT3.8 2564 

.................................................................................. WeCT3.4 2992 

.................................................................................. WeCT3.5 2998 

.................................................................................. WeDPT10.7 * 

.................................................................................. WeDPT10.8 * 

.................................................................................. ThDT5.3 5100 

Czarnowski, Justin.................................................... WeCT5.4 3101 

.................................................................................. WeDPT10.13 

D 

* 

D'Ambrosio, David.................................................... WeBT8.7 2802 

D'Andrea, Raffaello .................................................. ThDT6.1 5113 

.................................................................................. ThDT8.3 5179 

Daepp, Hannes......................................................... ThBT8.5 4556 

.................................................................................. ThCPT10.23 * 

Dagnino, Giulio......................................................... TuBT5.8 1359 

Dahl, Kristen............................................................. WeCT6.4 3140 

.................................................................................. WeDPT10.16 * 

Dahmen, Christian.................................................... MoAT1.4 19 

.................................................................................. MoBPT10.1 * 

.................................................................................. TuBT3.5 1297 

.................................................................................. TuCPT10.8 * 

Dalamagkidis, Konstantinos ..................................... TuAT7.8 1063 

.................................................................................. ThAT9.5 4148 

.................................................................................. ThBPT10.26 * 

DallaLibera, Fabio .................................................... WeDT3.6 3480 

.................................................................................. ThAPT10.9 * 

Dallej, Tej.................................................................. WeBT9.7 2855 

Daly, John Michael ................................................... ThCT8.6 4961 

.................................................................................. ThDPT10.24 * 

Damani, Aayush ....................................................... MoBT1.5 445 

.................................................................................. TuAPT10.2 * 

Danès, Patrick .......................................................... MoAT3 C 

.................................................................................. MoAT3 O 

.................................................................................. MoAT3.1 * 

.................................................................................. MoAT3.7 137 

.................................................................................. MoBT3 O 

Daniel, Bruce............................................................ WeBT3.8 2564 

Daniilidis, Kostas ...................................................... ThBT6.8 4469 

Dansereau, Donald Gilbert....................................... ThBT6.6 4455 

.................................................................................. ThCPT10.18 * 

Dario, Paolo.............................................................. TuBT3.1 * 

.................................................................................. ThCT5.5 4826 

.................................................................................. ThDPT10.14 * 

Darrell, Trevor........................................................... TuAT1 C 

.................................................................................. TuAT1.2 793 

.................................................................................. ThCT6.1 * 

.................................................................................. ThCT6.7 4877 

Das, Aditya ............................................................... TuCT3.7 1699 

Das, Aveek ............................................................... ThAT3.4 3874 


–236– 

.................................................................................. ThBPT10.7 * 

Das, Colin ................................................................. TuAT9.7 1160 

Das, Gautham .......................................................... ThCT2.8 4716 

Das, Jnaneshwar...................................................... WeCT6.3 3132 

Dastoor, Sanjay ........................................................ MoBT9.7 774 

David, Philip.............................................................. MoBT2.5 494 

.................................................................................. TuAPT10.5 * 

.................................................................................. TuCT2.3 1632 

Davison, Andrew J.................................................... ThCT6 O 

Dawids, Steen .......................................................... TuAT2.2 847 

de Almeida, Anibal.................................................... ThBT2.8 4277 

de Gea Fernandez, Jose .......................................... ThAT9.3 4134 

De Laet, Tinne .......................................................... WeCT4.2 3031 

De Lorenzo, Danilo................................................... WeBT4.3 2584 

De Luca, Alessandro ................................................ WeCT7 CC 

.................................................................................. WeCT7.4 3192 

.................................................................................. WeDPT10.19 * 

.................................................................................. ThAT7.1 4026 

de Menezes Pereira , Arvind A................................. WeCT6.5 3147 

de Menezes Pereira, Arvind A.................................. WeDPT10.17 * 

de Oliveira, Mauricio................................................. ThBT3.7 4329 

De Schutter, Bart ...................................................... WeBT7.3 2729 

De Schutter, Joris ..................................................... WeCT4.2 3031 

.................................................................................. ThCT2.3 4684 

de Vries, Jeroen ....................................................... MoBT1.1 421 

Debain, Christophe................................................... ThBT9.1 4569 

Decré, Wilm .............................................................. WeCT4.2 3031 

Del Prete, Andrea ..................................................... WeDT9.1 3694 

Delaney, John........................................................... WeCT6.1 * 

Delettre, Anne........................................................... ThDT6.2 5121 

Dellaert, Frank .......................................................... WeAT6.8 2242 

Dellepiane, Massimo ................................................ TuBT5.8 1359 

Delmerico, Jeffrey..................................................... TuCT2.3 1632 

DelPreto, Joseph ...................................................... TuBT6.5 1380 

.................................................................................. TuCPT10.14 * 

Demeester, Eric........................................................ WeCT3.1 2971 

Demiris, Yiannis........................................................ WeAT1.3 1980 

Demonceaux, Cédric ................................................ ThAT6.6 4006 

.................................................................................. ThBPT10.18 * 

Denei, Simone .......................................................... WeDT9.1 3694 

Deng, Xinyan ............................................................ MoAT5 CC 

.................................................................................. MoAT5 O 

.................................................................................. MoBT5 O 

.................................................................................. MoBT5.6 574 

.................................................................................. TuAPT10.12 * 

Denny, Jory .............................................................. WeBT5.5 2632 

.................................................................................. WeCPT10.14 * 

Derenick, Jason........................................................ WeCT8.4 3248 

.................................................................................. WeDT8.5 3667 

.................................................................................. WeDPT10.22 * 

.................................................................................. ThAPT10.20 * 

Dermitzakis, Konstantinos ........................................ WeDT3.2 3454 

Desai, Jaydev P........................................................ TuAT5 C 

.................................................................................. TuAT5 O 

.................................................................................. TuBT5 CC 

.................................................................................. TuBT5 O 

.................................................................................. TuBT5.1 * 

.................................................................................. TuCT5 C 

.................................................................................. TuCT5 O 

Desmaele, Denis ...................................................... TuAT3.6 913 

.................................................................................. TuBPT10.9 * 

Detry, Renaud .......................................................... TuBT9.7 1554 

Dettmann, Alexander................................................ WeDT8.3 3653 

Dey, Bir Bikram......................................................... ThDT3.3 5048 

Di Caro, Gianni A...................................................... ThCT9.1 4981 

.................................................................................. ThCT9.8 5027 

Di Lello, Enrico ......................................................... WeCT3.1 2971 

Di Mario, Ezequiel .................................................... ThBT4.1 4341 

Di Rocco, Maurizio ................................................... ThCT9.5 5007 

.................................................................................. ThDPT10.26 *

Di Stefano, Luigi ....................................................... ThCT6.4 4857 

.................................................................................. ThDPT10.16 * 

Dickmann, Jürgen..................................................... ThBT9.4 4587 

.................................................................................. ThCPT10.25 * 

Dietrich, Alexander ................................................... WeCT7.5 3199 

.................................................................................. WeDPT10.20 * 

Dille, Michael ............................................................ ThCT8.4 4947 

.................................................................................. ThDPT10.22 * 

Diller, Eric D.............................................................. TuBT3.4 1291 

.................................................................................. TuCT3.5 1687 

.................................................................................. TuCPT10.7 * 

.................................................................................. WeAPT10.8 * 

Dillmann, Rüdiger..................................................... TuCT6.2 1761 

.................................................................................. TuCT6.5 1781 

.................................................................................. WeAPT10.14 * 

.................................................................................. ThCT1.3 4633 

.................................................................................. ThCT5 C 

Dimitrov, Dimitar Nikolaev ........................................ TuCT6.7 1797 

.................................................................................. WeAT7.8 2292 

Ding, Xu Chu ............................................................ WeCT5.2 3087 

Dinont, Cédric........................................................... WeDT5.4 3519 

.................................................................................. ThAPT10.10 * 

Dissanayake, Gamini................................................ TuCT2.4 1640 

.................................................................................. WeAPT10.4 * 

.................................................................................. WeDT8.8 3686 

.................................................................................. ThBT9.7 4608 

Dittes, Benjamin ....................................................... TuAT7.1 1015 

Dobrzynski, Michal Karol.......................................... TuCT9.1 1913 

Doitsidis, Lefteris ...................................................... TuCT2.7 1661 

Dolha, Mihai Emanuel .............................................. WeCT7.1 3172 

Dollar, Aaron............................................................. TuBT6 C 

.................................................................................. TuBT6 O 

.................................................................................. TuBT7.3 1420 

.................................................................................. TuCT6 O 

.................................................................................. TuCT7.3 1823 

.................................................................................. WeBT6.1 2660 

Dominey, Peter Ford ................................................ WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Dong, Lixin................................................................ TuCT3.8 1705 

Dorigo, Marco........................................................... ThCT4.2 4762 

.................................................................................. ThCT9.8 5027 

Dörr, Jonas............................................................... WeDT3.5 3474 

.................................................................................. ThAPT10.8 * 

Doth, Klaus-Michael ................................................. ThDT8.1 5166 

Douillard, Bertrand.................................................... MoAT6.7 255 

Doyle, Courtney........................................................ ThCT8.8 4975 

Droge, Greg.............................................................. TuAT9.3 1134 

Du Toit, Noel E. ........................................................ WeCT8 C 

Ducatelle, Frederick.................................................. ThCT9.1 4981 

.................................................................................. ThCT9.8 5027 

Duchaine, Vincent .................................................... TuBT6.4 1373 

.................................................................................. TuCPT10.13 * 

Dudek, Gregory ........................................................ ThDT3.3 5048 

.................................................................................. ThDT3.4 5054 

Duff, Armin................................................................ TuAT8.8 1115 

Duhamel, Pierre-Emile ............................................. TuAT8.6 1099 

.................................................................................. TuBPT10.21 * 

Dupont, Pierre .......................................................... WeAT3.2 2083 

.................................................................................. ThBT7 C 

.................................................................................. ThBT7.6 4508 

.................................................................................. ThCPT10.21 * 

Dupuis, Erick ............................................................ MoBT6.8 631 

Duriez, Christian....................................................... WeBT4.7 2608 

Durrant-Whyte, Hugh................................................ TuAT2.8 886 

.................................................................................. ThBT2.2 

E 

4236 

Eathakota, Vijay........................................................ ThBT3.5 4314 

.................................................................................. ThCPT10.8 * 

Eck, Laurent ............................................................. ThCT8.1 4929 

Edsinger, Aaron........................................................ TuBT6 CC 


–237– 

.................................................................................. TuBT6 O 

.................................................................................. TuCT6 O 

Egerstedt, Magnus ................................................... TuAT9.3 1134 

.................................................................................. WeBT8.2 2772 

.................................................................................. ThBT8.5 4556 

.................................................................................. ThCPT10.23 * 

Einhorn, Erik ............................................................. WeBT1.5 2430 

Ek, Carl Henrik ......................................................... TuAT6.4 980 

.................................................................................. TuBPT10.13 * 

.................................................................................. WeAT2.2 2028 

Ekaterinaris, John A. ................................................ ThAT7.5 4054 

.................................................................................. ThBPT10.20 * 

Ekstrand, Fredrik ...................................................... TuCT2.2 1626 

Ekström, Mikael ........................................................ TuCT2.2 1626 

Elbrechter, Christof................................................... TuBT7.4 1427 

.................................................................................. TuCPT10.16 * 

Elkmann, Norbert...................................................... WeDT1.3 3355 

Emeli, Victor ............................................................. ThCT1.2 4627 

Ende, Tobias ............................................................ WeDT1.5 3367 

.................................................................................. ThAPT10.2 * 

Endo, Gen ................................................................ WeBT1.4 2423 

.................................................................................. WeCPT10.1 * 

Endo, Mitsuru ........................................................... ThBT9.5 4593 

.................................................................................. ThCPT10.26 * 

Eng, Dillon ................................................................ TuCT5.1 1711 

Engeberg, Erik Daniel............................................... ThBT1.1 4174 

Engel, Paulo ............................................................. TuAT9.1 1122 

Englsberger, Johannes............................................. ThBT5.7 4420 

Ergin, Mehmet Alper................................................. ThCT7.5 4917 

.................................................................................. ThDPT10.20 * 

Eruhimov, Victor ....................................................... WeCT2.4 2941 

.................................................................................. WeDPT10.4 * 

Esmaeili Malekabadi, Mohammad............................ ThCT7.3 4905 

Esparza, Noe............................................................ ThDT5.3 5100 

Espiau, Bernard........................................................ ThCT7 C 

Espinosa, Felipe ....................................................... ThAT1.1 3746 

Espinoza León, Judith .............................................. ThBT8.1 4528 

Esposito, Joel ........................................................... WeAT5.7 2192 

Estebanez, Belen ..................................................... WeAT3.8 2121 

Etoundi, Appolinaire C.............................................. ThAT7.3 4042 

Eustice, Ryan ........................................................... TuCT2 C 

.................................................................................. TuCT2.5 1647 

.................................................................................. WeAPT10.5 * 

.................................................................................. ThBT4.7 4378 

Evans, Nathan .......................................................... ThCT1.8 4664 

Even, Jani................................................................. MoBT3.6 536 

.................................................................................. TuAPT10.9 

F 

* 

Falcó Montesinos, Antonio ....................................... TuCT1.1 1567 

Falotico, Egidio ......................................................... ThCT5.5 4826 

.................................................................................. ThDPT10.14 * 

Fan, Zheng ............................................................... TuCT3.8 1705 

Fan, Zhun ................................................................. TuAT2.2 847 

Fankhauser, Peter .................................................... WeAT6.5 2223 

.................................................................................. WeBPT10.17 * 

Fantuzzi, Cesare ...................................................... ThBT9.8 4615 

Faria, Ricardo ........................................................... ThBT2.8 4277 

Fatikow, Sergej......................................................... MoAT1.4 19 

.................................................................................. MoBPT10.1 * 

.................................................................................. TuAT3.3 894 

.................................................................................. TuBT3.5 1297 

.................................................................................. TuCPT10.8 * 

Fatovic, Michael........................................................ TuAT3.8 925 

Fearing, Ronald ........................................................ WeBT6.3 2674 

.................................................................................. ThDT4.4 5080 

.................................................................................. ThDT5.1 5087 

Felekis, Dimitrios ...................................................... TuAT3.7 919 

Felfoul, Ouajdi .......................................................... TuBT3.6 1304 

.................................................................................. TuCPT10.9 * 

Felisa, Mirko ............................................................. TuCT1.6 1599

.................................................................................. WeAPT10.3 * 

Ferber, Jacques........................................................ ThDT3.2 5041 

Fernandez, Benito R................................................. WeAT7.4 2267 

.................................................................................. WeBPT10.19 * 

Ferrante, Eliseo ........................................................ ThCT9.8 5027 

Ferrary, Evelyne ....................................................... WeBT3.3 2532 

Ferreira, Antoine....................................................... TuBT3.5 1297 

.................................................................................. TuBT3.8 1315 

.................................................................................. TuCPT10.8 * 

Ferreira, Manuel ....................................................... WeAT7.7 2286 

Ferreira, Ricardo....................................................... ThCT5.5 4826 

.................................................................................. ThDPT10.14 * 

Ferretti, Gianni.......................................................... WeCT3.1 2971 

Ferrier, Nicola ........................................................... ThDT7.1 5139 

Ferrin, Jeffrey ........................................................... WeBT6.5 2688 

.................................................................................. WeCPT10.17 * 

.................................................................................. ThDT8.2 5173 

Ficuciello, Fanny....................................................... TuCT6.4 1775 

.................................................................................. WeAPT10.13 * 

Fierro, Rafael............................................................ WeAT8.6 2333 

.................................................................................. WeBPT10.24 * 

Fine, Benjamin.......................................................... ThCT9.4 5001 

.................................................................................. ThDPT10.25 * 

Finio, Benjamin......................................................... MoAT1.6 31 

.................................................................................. MoAT9.3 384 

.................................................................................. MoBPT10.3 * 

.................................................................................. TuAT8.6 1099 

.................................................................................. TuAT8.7 1107 

.................................................................................. TuBPT10.21 * 

Fink, Jonathan.......................................................... WeCT8.4 3248 

.................................................................................. WeDPT10.22 * 

Fiorini, Paolo............................................................. TuAT5 CC 

.................................................................................. TuAT5 O 

.................................................................................. TuAT5.1 * 

.................................................................................. TuBT5 C 

.................................................................................. TuBT5 O 

.................................................................................. TuCT5 CC 

.................................................................................. TuCT5 O 

Firman, Michael David.............................................. ThAT2.8 3850 

Fischer, Jan .............................................................. WeAT9.3 2365 

Fisher, Charles ......................................................... MoAT6.8 261 

Fjerdingen, Sigurd Aksnes ....................................... MoAT6.6 247 

.................................................................................. MoBPT10.18 * 

Flacco, Fabrizio ........................................................ ThAT7.1 4026 

Flemming, Leslie ...................................................... MoBT9.8 780 

Fleps, Michael .......................................................... WeCT9.3 3297 

Floreano, Dario......................................................... TuCT9 C 

.................................................................................. TuCT9.1 1913 

.................................................................................. ThCT9.6 5015 

.................................................................................. ThDPT10.27 * 

Folio, David............................................................... TuBT3.5 1297 

.................................................................................. TuCPT10.8 * 

Fontaine, Jean-Guy.................................................. ThAT5.4 3951 

.................................................................................. ThBPT10.13 * 

Forgoston, Eric ......................................................... ThAT4.3 3905 

Forlizzi, Jodi.............................................................. WeAT1.4 1986 

.................................................................................. WeBPT10.1 * 

Forsman, Pekka ....................................................... MoAT2.2 59 

Fourquet, Jean-Yves ................................................ ThAT9.2 4127 

Fox, Dieter................................................................ TuAT1.6 821 

.................................................................................. TuBPT10.3 * 

.................................................................................. ThAT6 CC 

.................................................................................. ThBT6 CC 

.................................................................................. ThCT6 C 

.................................................................................. ThCT6 O 

.................................................................................. ThCT6.3 4850 

Fox, Michael ............................................................. ThCT4.3 4770 

Fraichard, Thierry ..................................................... WeCT3.3 2985 

Fraisse, Philippe....................................................... ThCT1.7 4658 

Franchi, Antonio ....................................................... MoAT2 CC 


–238– 

.................................................................................. MoAT4.5 163 

.................................................................................. MoBT2.1 469 

.................................................................................. MoBPT10.11 * 

.................................................................................. WeAT6.4 2215 

.................................................................................. WeBPT10.16 * 

.................................................................................. WeCT4.3 3039 

Francisco, Gerald ..................................................... TuCT5.1 1711 

Frank, Barbara.......................................................... WeAT5.5 2180 

.................................................................................. WeBPT10.14 * 

Fraundorfer, Friedrich............................................... TuCT2.6 1655 

.................................................................................. WeAPT10.6 * 

.................................................................................. ThAT6.7 4012 

Frazzoli, Emilio ......................................................... MoAT7.6 298 

.................................................................................. MoBPT10.21 * 

.................................................................................. WeBT5.1 * 

.................................................................................. WeDT5.3 3513 

.................................................................................. ThBT3.4 4307 

.................................................................................. ThCPT10.7 * 

Freidovich, Leonid .................................................... ThDT5.2 5094 

Frese, Udo................................................................ TuCT1.4 1585 

.................................................................................. WeAPT10.1 * 

.................................................................................. WeCT9.4 3305 

.................................................................................. WeDT2.6 3426 

.................................................................................. WeDPT10.25 * 

.................................................................................. ThAPT10.6 * 

Friedl, Werner........................................................... TuBT6.3 1366 

.................................................................................. TuCT7.5 1836 

.................................................................................. WeAPT10.17 * 

.................................................................................. ThBT1.7 4215 

Friedman, Ariell ........................................................ TuBT9.4 1533 

.................................................................................. TuCPT10.22 * 

Fritz, Mario................................................................ TuAT1.2 793 

.................................................................................. ThCT6.7 4877 

Fu, Michael J. ........................................................... WeDT3.3 3460 

Fu, Yu ....................................................................... MoBT2.3 482 

Fu, Zhenbo ............................................................... TuAT3.8 925 

Fuchiwaki, Ohmi ....................................................... MoAT9.5 395 

.................................................................................. MoBT9 CC 

.................................................................................. MoBPT10.26 * 

Fujie, Masakatsu G................................................... TuAT5.6 955 

.................................................................................. TuBPT10.12 * 

Fujiki, Soichiro .......................................................... WeAT7.5 2274 

.................................................................................. WeAT7.6 2280 

.................................................................................. WeBPT10.20 * 

.................................................................................. WeBPT10.21 * 

Fujimoto, Hideo ........................................................ WeAT4.6 2145 

.................................................................................. WeBPT10.12 * 

Fujita, Emi................................................................. TuCT9.6 1946 

.................................................................................. WeAPT10.24 * 

Fukuda, Toshio......................................................... MoAT1.1 1 

.................................................................................. MoBT1.3 433 

.................................................................................. MoBT1.4 439 

.................................................................................. MoBT1.7 457 

.................................................................................. TuAT3.1 * 

.................................................................................. TuAPT10.1 * 

.................................................................................. TuCT3.6 1693 

.................................................................................. WeAT3.7 2115 

.................................................................................. WeAPT10.9 * 

.................................................................................. ThCT2.1 4670 

.................................................................................. ThDT5.2 5094 

Fukui, Rui ................................................................. WeDT2.5 3419 

.................................................................................. ThAPT10.5 * 

Fukushima, Edwardo F............................................. WeBT1.4 2423 

.................................................................................. WeCPT10.1 * 

Fukushima, Hiroaki................................................... TuCT8.8 1907 

Full, Robert ............................................................... MoAT5.1 * 

.................................................................................. TuCT8.5 1887 

.................................................................................. WeAPT10.20 * 

Fumagalli, Matteo ..................................................... ThAT9.7 4161 

Funakoshi, Kotaro .................................................... TuBT9.5 1540

.................................................................................. TuCPT10.23 * 

Fung, Henry.............................................................. WeAT7.3 2261 

Furtuna, Andrei......................................................... ThBT3.6 4321 

.................................................................................. ThCPT10.9 * 

Furuta, Takayuki....................................................... WeBT2.2 

G 

2466 

Gaboury, Louis ......................................................... TuBT3.6 1304 

.................................................................................. TuCPT10.9 * 

Gadre, Aditya............................................................ TuAT9.4 1140 

.................................................................................. TuBPT10.22 * 

Gaillard, François ..................................................... WeDT5.4 3519 

.................................................................................. ThAPT10.10 * 

Gal, Oren.................................................................. WeDT5.7 3539 

Galloway, Kevin........................................................ MoAT1.6 31 

.................................................................................. MoBPT10.3 * 

Galvez Lopez, Dorian............................................... MoAT2.1 51 

.................................................................................. TuBT2.5 1256 

.................................................................................. TuBT2.8 1277 

.................................................................................. TuCPT10.5 * 

Gambardella, Luca ................................................... ThCT9.1 4981 

.................................................................................. ThCT9.8 5027 

Gans, Nicholas ......................................................... TuBT1 C 

.................................................................................. TuBT1.2 1180 

Gao, Ce .................................................................... WeAT9.2 2359 

Gao, Yixin................................................................. WeBT3.4 2539 

.................................................................................. WeCPT10.7 * 

Garabini, Manolo ...................................................... ThAT1.5 3770 

.................................................................................. ThBPT10.2 * 

Garcia, Elena............................................................ TuBT8 C 

.................................................................................. TuBT8.8 1507 

Garcia Bermudez, Fernando .................................... WeBT6.3 2674 

Garney, Ben ............................................................. ThAT2.3 3816 

Gaspar, Jose ............................................................ ThCT5.5 4826 

.................................................................................. ThDPT10.14 * 

Gassert, Roger ......................................................... WeCT4.8 3074 

Gattupalli, Aditya ...................................................... ThBT3.5 4314 

.................................................................................. ThCPT10.8 * 

Gautier, Maxime ....................................................... WeDT9.6 3728 

.................................................................................. ThAPT10.24 * 

Gayet, Brice.............................................................. TuBT5.4 1333 

.................................................................................. TuBT5.5 1339 

.................................................................................. TuCPT10.10 * 

.................................................................................. TuCPT10.11 * 

Gehrig, Dirk .............................................................. ThCT5.4 4819 

.................................................................................. ThDPT10.13 * 

Geiger, James .......................................................... TuAT5.7 961 

Georgiev, Kristiyan ................................................... ThAT2.2 3808 

Geraerts, Roland ...................................................... WeDT5.5 3526 

.................................................................................. ThAPT10.11 * 

German, Christopher R. ........................................... MoAT6.8 261 

Gerratt, Aaron P. ...................................................... TuCT3.4 1680 

.................................................................................. WeAPT10.7 * 

Ghadiok, Vaibhav ..................................................... ThCT1.5 4645 

.................................................................................. ThDPT10.2 * 

Ghaffari Toiserkan, Kamran ..................................... WeDT9.8 3740 

Ghommam, Jawhar .................................................. WeAT8.7 2340 

Ghosh, Madhumita ................................................... WeBT1.2 2409 

Giardino, Simone...................................................... ThCT6.4 4857 

.................................................................................. ThDPT10.16 * 

Giertler, Bogumil....................................................... ThAT8.8 4115 

Gilbert, Hunter B....................................................... WeBT3.1 2517 

Gillies, Andrew G...................................................... ThDT5.1 5087 

Gillula, Jeremy.......................................................... WeCT3.2 2979 

Girbés, Vicent........................................................... ThBT3.8 4335 

Girdhar, Yogesh ....................................................... ThDT3.3 5048 

Giri, Nivedhitha......................................................... ThAT7.6 4060 

.................................................................................. ThBPT10.21 * 

Glasauer, Stefan....................................................... WeDT1.6 3375 

.................................................................................. ThAPT10.3 * 

Godage, Isuru S. ...................................................... TuAT8.5 1093 


–239– 

.................................................................................. TuBPT10.20 * 

Godin, Mike .............................................................. WeCT6.3 3132 

Goerick, Christian ..................................................... TuAT7.1 1015 

Goerner, Martin ........................................................ WeBT2.6 2497 

.................................................................................. WeCPT10.6 * 

Goeroeg, Attila.......................................................... ThCT5.8 4844 

Goldberg, Ken .......................................................... WeBT5.7 2646 

Goldfield, Eugene ..................................................... ThBT7.3 4488 

Goldin, Jeremy ......................................................... ThCT1.5 4645 

.................................................................................. ThDPT10.2 * 

Goldman, Roger E.................................................... WeAT9.7 2390 

Golombek, Raphael.................................................. WeCT3.7 3011 

Gomes, Kevin ........................................................... WeCT6.3 3132 

Gong, Zheng............................................................. MoBT3.5 530 

.................................................................................. TuAPT10.8 * 

Gonzalez, Yolanda ................................................... WeDT6.7 3577 

Gonzalez de Santos, Pablo ...................................... TuBT8.8 1507 

Goron, Lucian Cosmin.............................................. ThBT2.6 4263 

.................................................................................. ThCPT10.6 * 

Gosselin, Frederick P. .............................................. TuAT3.4 901 

.................................................................................. TuBPT10.7 * 

Goswami, Ambarish ................................................. ThAT5 C 

.................................................................................. ThAT5 O 

.................................................................................. ThAT5.3 3943 

Goto, Masataka ........................................................ WeAT1.6 2000 

.................................................................................. WeBPT10.3 * 

Gouttefarde, Marc..................................................... WeBT9.7 2855 

Gowal, Sven ............................................................. WeBT8.1 2765 

.................................................................................. WeCT9.5 3313 

.................................................................................. WeDPT10.26 * 

Graciano Santos, Vinicius ........................................ ThBT4.6 4372 

.................................................................................. ThCPT10.12 * 

Grand, Christophe .................................................... WeBT7.7 2753 

.................................................................................. ThBT4.4 4360 

.................................................................................. ThCPT10.10 * 

Grange, Sebastien.................................................... WeCT4.1 3023 

Gräser, Axel.............................................................. TuAT1.1 786 

Grebenstein, Markus ................................................ TuBT6.3 1366 

Gregory, John........................................................... WeAT4.4 2133 

.................................................................................. WeBPT10.10 * 

Grimmer, Martin........................................................ TuCT7.1 1811 

Grira, Aymen ............................................................ MoAT1.8 45 

Grisetti, Giorgio......................................................... MoAT2.6 86 

.................................................................................. MoBPT10.6 * 

.................................................................................. TuAT2.5 865 

.................................................................................. TuBPT10.5 * 

.................................................................................. WeDT9.4 3716 

.................................................................................. ThAPT10.22 * 

Grocholsky, Ben ....................................................... ThCT8.4 4947 

.................................................................................. ThDPT10.22 * 

Groeger, Martin ........................................................ ThBT7.5 4502 

.................................................................................. ThCPT10.20 * 

Gross, Horst-Michael................................................ WeBT1.5 2430 

.................................................................................. WeCPT10.2 * 

Grunberg, David ....................................................... WeCT1.8 2916 

Grupen, Rod ............................................................. WeDT1.2 3349 

Grzonka, Slawomir ................................................... TuBT2.4 1249 

.................................................................................. TuCPT10.4 * 

Gu, Ye ...................................................................... WeCT1.2 2873 

Guan, Yisheng.......................................................... TuBT8.3 1473 

Guerin, Kelleher........................................................ TuBT5.6 1346 

.................................................................................. TuCPT10.12 * 

Gueta, Lounell B....................................................... ThCT2.5 4698 

.................................................................................. ThDPT10.5 * 

Guglielmino, Emanuele ............................................ TuAT8.5 1093 

.................................................................................. TuBPT10.20 * 

.................................................................................. ThAT7.5 4054 

.................................................................................. ThBPT10.20 * 

Guitton, Julien........................................................... WeCT1.5 2895 

.................................................................................. WeDPT10.2 *

Guo, Shijie................................................................ WeBT1.7 2445 

Gupta, S.K................................................................ TuAT9 C 

Gupta, Satyandra K.................................................. TuAT9.6 1154 

.................................................................................. TuBPT10.24 * 

Gurdan, Daniel ......................................................... ThDT8.1 5166 

Gustafson, Joakim.................................................... WeDT1.1 3342 

Gutierrez, Alvaro....................................................... ThCT9.8 

H 

5027 

Ha, Hyowon.............................................................. WeCT9.2 3290 

Ha, Inyong ................................................................ WeCT7.6 3207 

.................................................................................. WeDPT10.21 * 

Haarnoja, Tuomas .................................................... WeAT7.2 2255 

Haberland, Matt........................................................ ThAT5.5 3957 

.................................................................................. ThBPT10.14 * 

Hach, Oliver.............................................................. ThAT8.1 4072 

Hacker, Franz........................................................... TuCT9.4 1933 

.................................................................................. WeAPT10.22 * 

Haddadin, Sami........................................................ TuCT6.6 1789 

.................................................................................. WeAPT10.15 * 

.................................................................................. WeDT1.5 3367 

.................................................................................. ThAPT10.2 * 

Hager, Gregory......................................................... WeAT3.5 2102 

.................................................................................. WeBT3.4 2539 

.................................................................................. WeBPT10.8 * 

.................................................................................. WeCT2.6 2953 

.................................................................................. WeCPT10.7 * 

.................................................................................. WeDPT10.6 * 

Hagita, Norihiro......................................................... MoBT3.6 536 

.................................................................................. MoBT3.8 550 

.................................................................................. TuAPT10.9 * 

.................................................................................. WeDT7.1 3589 

Hagiwara, Ichiro........................................................ MoBT3.5 530 

.................................................................................. TuAPT10.8 * 

Hagiwara, Masaya.................................................... TuBT3.7 1309 

Hagn, Ulrich.............................................................. WeCT4.1 3023 

Haines, Justin........................................................... MoAT6.3 227 

Halasz, Adam ........................................................... ThAT4.6 3923 

.................................................................................. ThBPT10.12 * 

Haliyo, Dogan Sinan................................................. TuAT3.3 894 

Halme, Aarne J......................................................... WeAT7.2 2255 

Hamann, Katharina................................................... WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Hamano, Seiya......................................................... WeDT2.2 3401 

Hammer, Peter ......................................................... TuBT5.3 1327 

Han, Chang-Soo....................................................... ThDT6 CC 

.................................................................................. ThDT6.3 5127 

Han, Jungsoo ........................................................... ThDT6.3 5127 

Han, Misop ............................................................... TuAT5.4 943 

.................................................................................. TuBPT10.10 * 

Han, Shuo................................................................. WeCT3.8 3017 

Han, Zhou................................................................. MoBT5.5 568 

.................................................................................. TuAPT10.11 * 

Handzhiyski, Nikolay ................................................ WeDT5.8 3545 

Häne, Christian......................................................... TuCT2.1 1618 

Hanebeck, Uwe D..................................................... WeAT2.5 2050 

.................................................................................. WeBPT10.5 * 

.................................................................................. WeCT4.5 3053 

.................................................................................. WeDPT10.11 * 

.................................................................................. ThCT5.4 4819 

.................................................................................. ThDPT10.13 * 

Hanheide, Marc ........................................................ WeCT3.7 3011 

.................................................................................. WeDT1.8 3387 

Hansen, Peter........................................................... ThAT6.8 4020 

Hanson, Allen ........................................................... WeDT1.2 3349 

Hanus, Jean Luc....................................................... TuBT3.8 1315 

Hao, Ning.................................................................. WeCT2.2 2928 

Hara, Masayuki......................................................... ThCT1.8 4664 

Harada, Tatsuya....................................................... TuBT1.7 1214 

Haraguchi, Daisuke .................................................. TuAT5.2 931 

Harata, Yuji............................................................... WeBT7.4 2735 


–240– 

.................................................................................. WeCPT10.19 * 

Hardy, Jason ............................................................ TuCT1.7 1605 

Haschke, Robert....................................................... TuBT7.4 1427 

.................................................................................. TuCPT10.16 * 

.................................................................................. WeCT2.5 2947 

.................................................................................. WeDPT10.5 * 

Hasegawa, Hiroaki ................................................... TuCT9.7 1954 

Hasegawa, Osamu ................................................... ThAT6.5 3998 

.................................................................................. ThBPT10.17 * 

Hasegawa, Tsutomu................................................. WeAT2.1 2020 

.................................................................................. ThBT1.5 4201 

.................................................................................. ThCPT10.2 * 

Hasegawa, Yasuhisa................................................ TuCT5.5 1737 

.................................................................................. WeAPT10.11 * 

.................................................................................. ThCT7.2 4899 

Häselich, Marcel ....................................................... WeDT5.8 3545 

Hashimoto, Kenji ...................................................... WeCT7.8 3221 

Hashimoto, Koichi..................................................... MoAT1 C 

.................................................................................. MoAT1.2 7 

Hassan Zahraee, Ali ................................................. TuBT5.4 1333 

.................................................................................. TuCPT10.10 * 

Hassen, Salhi ........................................................... WeCT3.3 2985 

Hassenzahl, Marc..................................................... WeDT1.5 3367 

Hatakeyama, Tomofumi ........................................... ThCT1.6 4652 

.................................................................................. ThDPT10.3 * 

Hatton, Ross............................................................. ThAT3.7 3893 

Hauert, Sabine.......................................................... ThCT9.6 5015 

.................................................................................. ThDPT10.27 * 

Hauser, Helmut......................................................... ThAT9.8 4168 

Hausknecht, Matthew ............................................... ThBT9.3 4581 

Hawes, Nick.............................................................. WeDT1.8 3387 

Hawkes, Elliot Wright................................................ ThDT5.3 5100 

Hayakawa, Yoshikazu .............................................. WeBT1.7 2445 

Hayashi, Atsushi....................................................... ThBT5.4 4400 

.................................................................................. ThCPT10.13 * 

Hayashi, Yoshiaki ..................................................... TuCT5.8 1755 

Hayashibe, Mitsuhiro ................................................ TuCT5.4 1731 

.................................................................................. WeAPT10.10 * 

.................................................................................. WeDT9.2 3701 

Healey, Anthony J. ................................................... WeCT6.6 3154 

.................................................................................. WeDPT10.18 * 

Heckmann, Martin .................................................... WeCT3.7 3011 

Hehn, Markus ........................................................... ThDT8.3 5179 

Heidarsson, Hordur K ............................................... WeCT6.7 3160 

Heimlich, Christian.................................................... ThDT4.3 5073 

Hellum, Aren............................................................. ThCT3.6 4749 

.................................................................................. ThDPT10.9 * 

Helmer, Patrick ......................................................... WeCT4.1 3023 

Heng, Lionel ............................................................. ThAT6.7 4012 

Hengst, Bernhard ..................................................... MoBT8.7 732 

Hennes, Daniel ......................................................... ThBT2.4 4249 

.................................................................................. ThCPT10.4 * 

Heracleous, Panikos................................................. MoBT3.6 536 

.................................................................................. TuAPT10.9 * 

Herbst, Evan............................................................. ThCT6.3 4850 

Herman, Benoît ........................................................ TuBT5.4 1333 

.................................................................................. TuBT5.5 1339 

.................................................................................. TuCPT10.10 * 

.................................................................................. TuCPT10.11 * 

Hermans, Tucker ...................................................... ThCT1.2 4627 

Hernandez Arieta, Alejandro .................................... WeDT3.2 3454 

Hernandez-Gutierrez, Andres................................... ThBT9.6 4601 

.................................................................................. ThCPT10.27 * 

Hershberger, Andrew D............................................ WeDT3.3 3460 

Hershey, John R....................................................... ThCT2.4 4690 

.................................................................................. ThDPT10.4 * 

Hertkorn, Katharina .................................................. TuCT6.3 1768 

Heyer, Clint............................................................... MoAT6.4 235 

.................................................................................. MoBPT10.16 * 

Heyneman, Barrett ................................................... TuBT6.4 1373

.................................................................................. TuCPT10.13 * 

Higashimori, Mitsuru................................................. TuBT6.6 1386 

.................................................................................. TuBT6.7 1392 

.................................................................................. TuCPT10.15 * 

.................................................................................. ThAT7.4 4048 

.................................................................................. ThBPT10.19 * 

Higuchi, Toshiro........................................................ ThCT1.8 4664 

Hilario Pérez, Lucía .................................................. TuCT1.1 1567 

Hilsenbeck, Barbara ................................................. WeCT1.6 2903 

.................................................................................. WeDPT10.3 * 

Himmelsbach, Michael ............................................. ThBT8.6 4562 

.................................................................................. ThCPT10.24 * 

Hirai, Hiroaki............................................................. ThBT7.4 4496 

.................................................................................. ThCPT10.19 * 

Hirai, Shinichi............................................................ TuCT9.6 1946 

.................................................................................. WeAPT10.24 * 

Hirano, Shinya.......................................................... WeBT1.7 2445 

Hirata, Yasuhisa ....................................................... MoAT7 C 

.................................................................................. MoAT7.8 311 

.................................................................................. ThBT9.5 4593 

.................................................................................. ThCPT10.26 * 

Hirche, Sandra.......................................................... MoAT8.5 344 

.................................................................................. MoBPT10.23 * 

.................................................................................. WeBT1.3 2416 

Hirose, Shigeo.......................................................... MoAT5.2 * 

.................................................................................. TuAT8.3 1081 

.................................................................................. TuPL.1 * 

.................................................................................. WeBT1.4 2423 

.................................................................................. WeCPT10.1 * 

Hirschmüller, Heiko .................................................. WeBT2.6 2497 

.................................................................................. WeCPT10.6 * 

Hirzinger, Gerd ......................................................... MoAT4.7 177 

.................................................................................. TuAT6.3 973 

.................................................................................. TuAT7.7 1055 

.................................................................................. TuPL.1 * 

.................................................................................. TuCT6.3 1768 

.................................................................................. TuCT7.5 1836 

.................................................................................. WeAT4.8 2158 

.................................................................................. WeAPT10.17 * 

.................................................................................. WeCT4.1 3023 

.................................................................................. WeDT9.3 3708 

.................................................................................. ThBT5.7 4420 

.................................................................................. ThBT7.5 4502 

.................................................................................. ThCPT10.20 * 

Ho, Sean................................................................... MoBT2.5 494 

.................................................................................. TuAPT10.5 * 

Ho, Van..................................................................... TuCT9.6 1946 

.................................................................................. WeAPT10.24 * 

Hochgeschwender, Nico........................................... TuAT7.3 1030 

Hodgson, Sean......................................................... MoBT9.1 738 

Hoepflinger, Mark ..................................................... MoBT5.4 562 

.................................................................................. TuAPT10.10 * 

Hoeppner, Hannes ................................................... TuCT7.5 1836 

.................................................................................. WeAPT10.17 * 

Hoff, Nicholas ........................................................... ThCT9.2 4989 

Hoffman, Judy .......................................................... WeBT5.7 2646 

Hoffman, Katie.......................................................... TuBT8.4 1479 

.................................................................................. TuCPT10.19 * 

Holenstein, Claude ................................................... ThAT2.5 3830 

.................................................................................. ThBPT10.5 * 

Hollinger, Geoffrey.................................................... WeDT6.5 3564 

.................................................................................. ThAPT10.14 * 

.................................................................................. ThBT8 CC 

Holz, Dirk.................................................................. ThBT2.4 4249 

.................................................................................. ThCPT10.4 * 

Homma, Michio......................................................... MoAT1.1 1 

.................................................................................. TuCT3.6 1693 

.................................................................................. WeAPT10.9 * 

Hong, Dennis............................................................ ThAT5.6 3963 

.................................................................................. ThBPT10.15 * 


–241– 

Hoon, Kay Hiang ...................................................... ThCT7.6 4923 

.................................................................................. ThDPT10.21 * 

Hoover, Randy.......................................................... WeCT2.2 2928 

Horchler, Andrew...................................................... MoAT5.4 197 

.................................................................................. MoBPT10.13 * 

Hornung, Armin ........................................................ ThCT5.8 4844 

Hoshino, Satoshi ...................................................... WeAT8.3 2314 

.................................................................................. WeBT8.8 2810 

Hosoda, Koh............................................................. ThCT5.7 4838 

Hosoi, Anette ............................................................ ThAT3.7 3893 

Howard, Andrew ....................................................... ThBT6.2 * 

Howe, Robert D. ....................................................... TuBT5.3 1327 

.................................................................................. WeDT3.4 3468 

.................................................................................. ThAPT10.7 * 

Hrabar, Stefan .......................................................... ThCT8 C 

.................................................................................. ThCT8.7 4967 

Hsieh, M. Ani ............................................................ ThAT4 C 

.................................................................................. ThAT4 O 

.................................................................................. ThBT4 C 

.................................................................................. ThBT4 O 

.................................................................................. ThCT4 O 

.................................................................................. ThCT4.1 * 

.................................................................................. ThCT4.6 4790 

.................................................................................. ThDPT10.12 * 

Hu, Chengzhi............................................................ MoBT1.4 439 

.................................................................................. TuAPT10.1 * 

Hu, Tianjiang ............................................................ MoBT5.5 568 

.................................................................................. TuAPT10.11 * 

Hu, Yonghui.............................................................. TuCT8.1 1863 

Hu, Zheng................................................................. MoBT5.6 574 

.................................................................................. TuAPT10.12 * 

Huang, Guoquan ...................................................... MoAT2.3 65 

Huang, Han-Pang..................................................... MoAT9 C 

.................................................................................. MoAT9.1 372 

Huang, Jian .............................................................. ThCT2.1 4670 

Huang, Ke................................................................. MoAT4.3 149 

Huang, Ke Jung........................................................ TuBT8.6 1493 

.................................................................................. TuCPT10.21 * 

Huang, Shoudong..................................................... WeDT8.8 3686 

Huang, Shuo............................................................. TuCT2.8 1668 

Huang, Tzu-Hao ....................................................... MoAT9.1 372 

Huang, Yanjiang ....................................................... ThCT2.5 4698 

.................................................................................. ThDPT10.5 * 

Huang, Yanlong........................................................ WeDT2.7 3434 

Huang, Yazhou......................................................... WeBT5.8 2653 

Huber, Daniel............................................................ WeCT9.1 3283 

Huber, Markus .......................................................... WeDT1.6 3375 

.................................................................................. ThAPT10.3 * 

Hudas, Greg ............................................................. ThAT8.4 4091 

.................................................................................. ThBPT10.22 * 

Huebner, Kai............................................................. TuAT6.4 980 

.................................................................................. TuBPT10.13 * 

Huhle, Benjamin ....................................................... ThBT2.1 4229 

Huihua, Zhao ............................................................ TuCT5.3 1723 

Hunt, Alexander J ..................................................... MoAT5.6 209 

.................................................................................. MoBPT10.15 * 

Huntsberger, Terry ................................................... ThCT3 CC 

.................................................................................. ThCT3.3 4728 

Huo, Xiaoming .......................................................... WeDT5.2 3507 

.................................................................................. WeDT5.6 3533 

.................................................................................. ThAPT10.12 * 

Hurst, Jonathan ........................................................ ThAT1 CC 

.................................................................................. ThAT1.3 3758 

Hürzeler, Christoph................................................... WeBT6.6 2694 

.................................................................................. WeCPT10.18 * 

Hutchinson, Seth ...................................................... WeAT2.8 2070 

.................................................................................. WeBT9 CC 

.................................................................................. WeBT9.1 2817 

Hutter, Marco............................................................ MoBT5.4 562 

.................................................................................. TuAPT10.10 *

Hyon, Sang-Ho......................................................... ThAT5.8 

I 

3975 

Iagnemma, Karl ........................................................ MoAT7.6 298 

.................................................................................. MoBPT10.21 * 

.................................................................................. ThAT8 C 

.................................................................................. ThAT8.4 4091 

.................................................................................. ThBPT10.22 * 

Ichikawa, Akihiko ...................................................... MoAT1.5 25 

.................................................................................. MoBPT10.2 * 

Ideta, Aiko................................................................. WeBT7.1 2715 

Igarashi, Yasunobu................................................... MoAT1.2 7 

Iida, Fumiya .............................................................. ThDT5 C 

.................................................................................. ThDT5.4 5107 

Iimura, Taiki .............................................................. ThBT7.4 4496 

.................................................................................. ThCPT10.19 * 

Ijspeert, Auke............................................................ TuCT8.7 1901 

Ikeda, Atsutoshi........................................................ WeAT4.3 2127 

Ikeda, Seiichi ............................................................ MoBT1.4 439 

.................................................................................. TuAPT10.1 * 

.................................................................................. WeAT3.7 2115 

Ikeda, Takeshi .......................................................... ThDT6.4 5133 

Ikeda, Tetsushi ......................................................... WeDT7.1 3589 

Ikedo, Norio .............................................................. WeAT4.6 2145 

.................................................................................. WeBPT10.12 * 

Iliev, Boyko ............................................................... TuCT6.7 1797 

Iliopoulos, Konstantinos............................................ WeAT9.7 2390 

Imamura, Nobuaki .................................................... TuCT5.2 1717 

Imura, Jun-ichi .......................................................... MoAT3.2 106 

.................................................................................. MoAT3.6 131 

.................................................................................. MoBPT10.9 * 

Inahara, Tomoyuki.................................................... TuBT6.7 1392 

Inamura, Tetsunari ................................................... WeDT1 C 

.................................................................................. WeDT1.7 3381 

Ince, Gokhan ............................................................ MoAT3.2 106 

.................................................................................. MoAT3.6 131 

.................................................................................. MoAT3.8 143 

.................................................................................. MoBPT10.9 * 

Inoue, Keita .............................................................. ThBT7.4 4496 

.................................................................................. ThCPT10.19 * 

Inoue, Ryosuke......................................................... WeBT7.6 2747 

.................................................................................. WeCPT10.21 * 

Iocchi, Luca .............................................................. WeBT8.6 2796 

.................................................................................. WeCPT10.24 * 

Iribe, Masatsugu ....................................................... WeBT1.4 2423 

.................................................................................. WeCPT10.1 * 

Ishi, Carlos Toshinori................................................ MoBT3.6 536 

.................................................................................. MoBT3.8 550 

.................................................................................. TuAPT10.9 * 

Ishigami, Genya........................................................ MoBT6.3 601 

.................................................................................. ThAT8.4 4091 

.................................................................................. ThBPT10.22 * 

Ishiguro, Akio............................................................ TuCT8 C 

.................................................................................. TuCT8.3 1875 

.................................................................................. TuCT8.4 1881 

.................................................................................. TuCT8.6 1895 

.................................................................................. WeAPT10.19 * 

.................................................................................. WeAPT10.21 * 

Ishiguro, Hiroshi........................................................ MoBT3.8 550 

.................................................................................. WeCT1.1 2867 

.................................................................................. WeDT3.6 3480 

.................................................................................. ThAPT10.9 * 

Ishii, Idaku ................................................................ TuBT1.6 1208 

.................................................................................. TuCPT10.3 * 

Ishikawa, Jun............................................................ WeAT4.7 2151 

Ishikawa, Masatoshi ................................................. TuCT9.7 1954 

Iskakov, Renat.......................................................... ThBT7.5 4502 

.................................................................................. ThCPT10.20 * 

Isler, Volkan.............................................................. MoBT2.4 488 

.................................................................................. TuAPT10.4 * 

Isom, Taylor.............................................................. ThCT8.8 4975 


–242– 

Ito, Masaki ................................................................ MoBT1.3 433 

Itohara, Tatsuhiko..................................................... MoAT3.4 118 

.................................................................................. MoBPT10.7 * 

Itti, Laurent................................................................ WeAT9.8 2397 

Ivaldi, Serena............................................................ ThBT4.3 4354 

Iwahashi, Naoto........................................................ MoAT8.6 350 

.................................................................................. MoBPT10.24 * 

.................................................................................. TuBT9.2 1520 

.................................................................................. TuBT9.5 1540 

.................................................................................. TuCPT10.23 * 

Iwasa, Shingo ........................................................... TuCT7.6 1843 

.................................................................................. WeAPT10.18 * 

Iwashita, Yumi .......................................................... WeAT2.1 2020 

Iwata, Hiroyasu......................................................... WeDT9.7 3734 

Izutsu, Masaki........................................................... WeAT4.7 

J 

2151 

Jääskeläinen, Mirva.................................................. TuAT3.5 907 

.................................................................................. TuBPT10.8 * 

Jahn, Benjamin......................................................... TuAT6.3 973 

Jahya, Alex ............................................................... WeBT3.7 2557 

Jaillet, Leonard ......................................................... WeBT5.7 2646 

Jäkel, Rainer............................................................. ThCT1.3 4633 

Jakuba, Michael........................................................ MoAT6.8 261 

.................................................................................. TuBT2.3 1242 

.................................................................................. ThCT3.2 4722 

Jaleel, Hassan .......................................................... WeBT8.2 2772 

Janicek, Miroslav ...................................................... WeDT1.8 3387 

Janoch, Allison ......................................................... TuAT1.2 793 

Jäntsch, Michael....................................................... TuAT7.8 1063 

.................................................................................. ThAT9.5 4148 

.................................................................................. ThBPT10.26 * 

Jarquín, Gerardo ...................................................... TuCT1.8 1612 

Jarrault, Pierre .......................................................... WeBT7.7 2753 

Jens, Kristensen ....................................................... TuAT2.2 847 

Jensfelt, Patric .......................................................... TuBT9.1 1513 

Jentoft, Leif ............................................................... WeDT3.4 3468 

Jentoft, Leif P............................................................ ThAPT10.7 * 

Jeong, Jaeseung ...................................................... MoBT7.8 685 

Jeong, Jin Hyeok ...................................................... TuAT5.8 967 

Jeong, Yekeun.......................................................... WeCT9.2 3290 

.................................................................................. ThBT6.3 4436 

Jia, Peifa................................................................... WeAT9.2 2359 

Jia, Yangqing............................................................ TuAT1.2 793 

Jia, Yunyi .................................................................. MoAT4.6 171 

.................................................................................. MoAT4.8 184 

.................................................................................. MoBPT10.12 * 

Jia, Zhenzhong ......................................................... WeCT3.6 3004 

.................................................................................. WeDPT10.9 * 

Jiang, Jinn-Feng ....................................................... TuCT9.8 1962 

Jiang, Li .................................................................... TuBT8.3 1473 

Jo, Sungho ............................................................... MoBT7.8 685 

Joerg, Stefan ............................................................ TuAT7.7 1055 

Jog, Amod ................................................................ WeBT3.4 2539 

.................................................................................. WeCPT10.7 * 

Johansson, Rolf........................................................ ThCT2 CC 

.................................................................................. ThCT2.6 4704 

.................................................................................. ThDPT10.6 * 

Johns, Edward.......................................................... MoBT3.7 542 

Johnson, Chris.......................................................... ThCT8.8 4975 

Johnson, David......................................................... MoBT9.8 780 

Johnson-Roberson, Matthew.................................... WeDT1.1 3342 

Joly, Cyril .................................................................. WeCT9.6 3320 

.................................................................................. WeDPT10.27 * 

Jones, Burton ........................................................... WeCT6.5 3147 

.................................................................................. WeDPT10.17 * 

Jonker, Pieter ........................................................... TuAT6.6 995 

.................................................................................. TuBPT10.15 * 

Jørgensen, Jimmy Alison ......................................... TuAT6.5 987 

.................................................................................. TuBPT10.14 * 

Jouvencel, Bruno...................................................... WeDT6.4 3558

.................................................................................. ThDT3.2 5041 

Ju, Wendy................................................................. WeBT1.8 2452 

Julian, Brian.............................................................. ThDT8.4 5187 

Julier, Simon Justin .................................................. ThAT2.8 3850 

Jung, Eui-jung........................................................... WeDT7.2 3595 

Jung, Jinwoo............................................................. ThDT7.1 5139 

Jung, Jiyoung ........................................................... WeCT9.2 

K 

3290 

Kadivar, Zahra.......................................................... TuCT5.1 1711 

Kagami, Satoshi ....................................................... ThBT5 O 

.................................................................................. ThCT5 O 

Kahn, Jeff ................................................................. MoBT5.7 580 

Kajita, Shuuji............................................................. WeAT1.6 2000 

.................................................................................. WeBPT10.3 * 

.................................................................................. ThAT5.1 * 

.................................................................................. ThBT5.3 4392 

.................................................................................. ThBT5.8 4428 

Kalakrishnan, Mrinal................................................. MoAT8.2 325 

.................................................................................. MoAT8.8 365 

.................................................................................. ThCT1.4 4639 

.................................................................................. ThDPT10.1 * 

Kallem, Vinutha ........................................................ WeCT5.8 3126 

Kallman, Jason......................................................... ThCT8.8 4975 

Kallmann, Marcelo.................................................... WeBT5.8 2653 

Kam, Moshe ............................................................. WeAT8.2 2306 

Kamamichi, Norihiro ................................................. WeAT4.7 2151 

Kamezaki, Mitsuhiro ................................................. WeDT9.7 3734 

Kammel, Sören......................................................... TuBT7.2 1413 

Kanade, Takeo ......................................................... WeCT9.1 3283 

Kanani, Keyvan ........................................................ MoBT6.6 619 

.................................................................................. TuAPT10.15 * 

Kanbayashi, Takashi ................................................ ThBT9.5 4593 

.................................................................................. ThCPT10.26 * 

Kanehira, Noriyuki .................................................... ThBT5.4 4400 

.................................................................................. ThCPT10.13 * 

Kanehiro, Fumio ....................................................... ThBT5.3 4392 

.................................................................................. ThBT5.4 4400 

.................................................................................. ThBT5.8 4428 

.................................................................................. ThCPT10.13 * 

Kaneko, Kenji ........................................................... ThBT5.3 4392 

.................................................................................. ThBT5.4 4400 

.................................................................................. ThBT5.8 4428 

.................................................................................. ThCPT10.13 * 

Kaneko, Makoto........................................................ TuBT6.6 1386 

.................................................................................. TuBT6.7 1392 

.................................................................................. TuCPT10.15 * 

.................................................................................. ThAT7.4 4048 

.................................................................................. ThBPT10.19 * 

Kang, Rongjie........................................................... ThAT7.5 4054 

.................................................................................. ThBPT10.20 * 

Kang, Sungchul ........................................................ TuCT7.8 1857 

Kano, Takeshi........................................................... TuCT8.3 1875 

.................................................................................. TuCT8.4 1881 

.................................................................................. TuCT8.6 1895 

.................................................................................. WeAPT10.19 * 

.................................................................................. WeAPT10.21 * 

Kanou, Kazuki .......................................................... TuAT5.6 955 

.................................................................................. TuBPT10.12 * 

Kanoulas, Dimitrios................................................... TuBT7.6 1439 

.................................................................................. TuCPT10.18 * 

Kantor, George......................................................... MoBT2.3 482 

.................................................................................. TuBT5.7 1353 

.................................................................................. ThDT7.2 5147 

Kao, Ching-Chung.................................................... WeCT2.8 2965 

Kapadia, Apoorva..................................................... TuAT8.4 1087 

.................................................................................. TuBPT10.19 * 

Kapoor, Ankur........................................................... MoBT7.1 639 

Kappler, Daniel......................................................... TuCT6.2 1761 

Karaman, Sertac....................................................... WeDT5.3 3513 

.................................................................................. ThBT3.4 4307 


–243– 

.................................................................................. ThCPT10.7 * 

Karaoguz, Cem......................................................... TuBT1.3 1187 

Karayev, Sergey ....................................................... TuAT1.2 793 

Karino, Takahiro ....................................................... ThBT8.4 4550 

.................................................................................. ThCPT10.22 * 

Karpelson, Michael ................................................... ThDT4.3 5073 

Karssen, Daniël ........................................................ ThAT5.5 3957 

.................................................................................. ThBPT10.14 * 

Kashioka, Hideki....................................................... MoAT8.6 350 

.................................................................................. MoBPT10.24 * 

Kashiwazaki, Koshi................................................... ThBT9.5 4593 

.................................................................................. ThCPT10.26 * 

Katayama, Daiki ....................................................... WeAT7.6 2280 

.................................................................................. WeBPT10.21 * 

Kato, Shin ................................................................. ThAT8.7 4109 

Katsev, Max.............................................................. WeBT2.8 2511 

Katsuki, Yoshio......................................................... ThDT6.4 5133 

Kavraki, Lydia ........................................................... ThCT1.1 4621 

Kawahara, Tomohiro ................................................ TuBT3.7 1309 

Kawakami, Daiki ....................................................... MoAT1.5 25 

.................................................................................. MoBPT10.2 * 

Kawamura, Akihiro ................................................... ThBT1.5 4201 

.................................................................................. ThCPT10.2 * 

Kawamura, Sadao .................................................... ThCT3.7 4756 

Kawashima, Kenji ..................................................... TuAT5.2 931 

Kawato, Mitsuo ......................................................... ThAT5.8 3975 

Kazakidi, Asimina ..................................................... ThAT7.5 4054 

.................................................................................. ThBPT10.20 * 

Kazanzides, Peter .................................................... MoBT7.1 639 

Kazerooni, Homayoon .............................................. ThCT7.4 4911 

.................................................................................. ThDPT10.19 * 

Kazmitcheff, Guillaume............................................. WeBT3.3 2532 

Keith, François.......................................................... ThAT3.6 3887 

.................................................................................. ThBPT10.9 * 

Keller, Thomas ......................................................... WeDT1.8 3387 

Kelly, Alonzo............................................................. MoAT6 O 

.................................................................................. MoBT6 O 

.................................................................................. WeAT5.4 2172 

.................................................................................. WeBPT10.13 * 

Kelly, Jonathan ......................................................... WeCT9 C 

Kendoul, Farid .......................................................... ThCT8.5 4953 

.................................................................................. ThDPT10.23 * 

Kermani, Mehrdad R. ............................................... WeBT3.6 2551 

.................................................................................. WeCPT10.9 * 

Kermorgant, Olivier................................................... MoBT9.6 768 

.................................................................................. TuAPT10.24 * 

.................................................................................. WeBT9.6 2849 

.................................................................................. WeCPT10.27 * 

Kersbergen, Bart ...................................................... WeBT7.3 2729 

Keshmiri, Mehdi........................................................ ThAT1.2 3752 

Keuning, Jasper David ............................................. MoBT1.1 421 

Khansari-Zadeh, Seyed Mohammad........................ MoBT8.4 710 

.................................................................................. TuAPT10.19 * 

Khatib, Oussama ...................................................... TuAT7.4 1036 

.................................................................................. TuBPT10.16 * 

.................................................................................. TuCT7.4 1830 

.................................................................................. WeAPT10.16 * 

.................................................................................. WeCT3.4 2992 

.................................................................................. WeCT3.5 2998 

.................................................................................. WeDPT10.7 * 

.................................................................................. WeDPT10.8 * 

Kheddar, Abderrahmane .......................................... ThAT3.6 3887 

.................................................................................. ThBT5.6 4414 

.................................................................................. ThBPT10.9 * 

.................................................................................. ThCPT10.15 * 

Khushaba, Rami ....................................................... ThBT9.7 4608 

Kielhöfer, Simon ....................................................... TuCT9.4 1933 

.................................................................................. WeAPT10.22 * 

Kiesler, Sara ............................................................. WeAT1.4 1986 

.................................................................................. WeBPT10.1 *

Kiguchi, Kazuo.......................................................... TuCT5.8 1755 

Kim, Ayoung ............................................................. TuCT2.5 1647 

.................................................................................. WeAPT10.5 * 

Kim, Chunwoo .......................................................... TuAT5.4 943 

.................................................................................. TuBPT10.10 * 

Kim, Chyon Hae ....................................................... ThAT1.8 3792 

Kim, Dongwook ........................................................ TuCT3.3 1674 

Kim, Eunyoung ......................................................... ThAT2.1 3800 

Kim, H. Jin ................................................................ WeBT8.5 2790 

.................................................................................. WeCPT10.23 * 

Kim, James Dokyoon................................................ WeCT9.2 3290 

Kim, Jung.................................................................. ThBT7 CC 

.................................................................................. ThBT7.7 4516 

Kim, Junsuk.............................................................. WeCT4.3 3039 

Kim, Keehoon........................................................... ThBT7.2 4483 

Kim, Min Jeong......................................................... TuBT7.7 1447 

Kim, Minjun............................................................... WeBT3.2 2524 

Kim, Sangbae........................................................... ThAT5.5 3957 

.................................................................................. ThBPT10.14 * 

Kim, Sungmin ........................................................... WeBT3.2 2524 

Kim, Ui-Hyun ............................................................ WeCT1.7 2910 

Kim, Woojin .............................................................. WeBT8.5 2790 

.................................................................................. WeCPT10.23 * 

Kim, Yeongjin ........................................................... ThBT7.7 4516 

Kim, Young Soo........................................................ WeBT3.2 2524 

Kim, Youngmoo........................................................ WeCT1.8 2916 

King, Raymond......................................................... ThCT8.8 4975 

Kinnaert, Michel........................................................ ThBT7.1 4477 

Kinsey, James .......................................................... MoAT6.8 261 

.................................................................................. ThCT3.2 4722 

Kirchner, Frank......................................................... ThAT9 CC 

.................................................................................. ThAT9.3 4134 

Kirchner, Nathan....................................................... WeCT1.6 2903 

.................................................................................. WeDPT10.3 * 

Kit, Dmitry................................................................. TuBT1.4 1194 

.................................................................................. TuCPT10.1 * 

Kitt, Bernd................................................................. MoAT6.3 227 

Klappstein, Jens ....................................................... ThBT9.4 4587 

.................................................................................. ThCPT10.25 * 

Kleiner, Alexander .................................................... WeCT8.8 3276 

Klimchik, Alexandr.................................................... ThAT7.2 4034 

Klodmann, Julian...................................................... WeDT9.3 3708 

Klotzbuecher, Markus............................................... ThCT2.3 4684 

Kneip, Laurent .......................................................... MoAT2.5 79 

.................................................................................. MoBPT10.5 * 

.................................................................................. WeAT6.7 2235 

.................................................................................. WeBT6.6 2694 

.................................................................................. WeCPT10.18 * 

Knepper, Ross A ...................................................... ThAT3.1 3856 

Knoll, Alois................................................................ TuAT7.8 1063 

.................................................................................. WeDT1.6 3375 

.................................................................................. ThAT9.5 4148 

.................................................................................. ThAPT10.3 * 

.................................................................................. ThBPT10.26 * 

Ko, Hanseok............................................................. MoAT3 O 

.................................................................................. MoBT3 O 

Kobayashi, Ryo ........................................................ TuCT8.3 1875 

Kobayashi, Yo .......................................................... TuAT5.6 955 

.................................................................................. TuBPT10.12 * 

Kober, Jens .............................................................. MoAT8.4 338 

.................................................................................. MoBPT10.22 * 

Kocamaz, Mehmet.................................................... ThAT8.3 4084 

Kodagoda, Sarath..................................................... ThBT9.7 4608 

Kodama, Atsushi ...................................................... MoAT7.1 268 

Koehler, Sarah.......................................................... WeCT5.7 3120 

Koepl, Devin ............................................................. ThAT1.3 3758 

Koh, Kyoung-Chul .................................................... TuAT5.8 967 

Kohda, Takehisa....................................................... WeAT7.5 2274 

.................................................................................. WeAT7.6 2280 

.................................................................................. WeBPT10.20 * 


–244– 

.................................................................................. WeBPT10.21 * 

Kohler, Norman ........................................................ ThCT8.3 4941 

Kojima, Masaru......................................................... MoAT1.1 1 

.................................................................................. MoBT1.3 433 

.................................................................................. MoBT1.7 457 

.................................................................................. TuCT3.6 1693 

.................................................................................. WeAPT10.9 * 

Kokuryu, Saori .......................................................... WeAT4.7 2151 

Komori, Kimihiro ....................................................... WeAT3.7 2115 

Kondak, Konstantin .................................................. WeDT3.5 3474 

.................................................................................. ThAPT10.8 * 

Kondo, Daisuke ........................................................ TuCT9.6 1946 

.................................................................................. WeAPT10.24 * 

Kondo, Hayato.......................................................... WeCT6 O 

.................................................................................. WeDT6 O 

Kondo, Hideki ........................................................... WeCT7.8 3221 

Kondo, Takao ........................................................... MoAT1.1 1 

Kong, Kyoungchul .................................................... WeBT2.3 2474 

Kong, Zhaodan ......................................................... WeCT5.3 3093 

Konietschke, Rainer ................................................. WeDT9.3 3708 

Königs, Achim........................................................... WeDT7.5 3614 

.................................................................................. ThAPT10.17 * 

Konolige, Kurt ........................................................... ThBT2.4 4249 

.................................................................................. ThCPT10.4 * 

Konyo, Masashi ........................................................ WeDT3 CC 

.................................................................................. WeDT3.7 3488 

.................................................................................. WeDT3.8 3494 

Koo, Ja Choon.......................................................... TuBT7.7 1447 

Kootstra, Gert ........................................................... TuAT6.5 987 

.................................................................................. TuBPT10.14 * 

Kormushev, Petar..................................................... MoAT8 CC 

.................................................................................. MoAT8.1 318 

Koropouli, Vasiliki ..................................................... MoAT8.5 344 

.................................................................................. MoBPT10.23 * 

Kosecka, Jana .......................................................... WeAT9 C 

.................................................................................. WeAT9.6 2383 

.................................................................................. WeBPT10.27 * 

Koseki, Yoshihiko ..................................................... WeBT4.3 2584 

Kosmatopoulos, Elias ............................................... TuCT2.7 1661 

Kossyk, Ingo ............................................................. WeDT3.5 3474 

.................................................................................. ThAPT10.8 * 

Kosuge, Kazuhiro ..................................................... MoAT7.8 311 

.................................................................................. WeAT1.7 2008 

.................................................................................. ThBT9.5 4593 

.................................................................................. ThCPT10.26 * 

Kotoku, Tetsuo ......................................................... TuAT7.5 1042 

.................................................................................. TuBPT10.17 * 

Kovecses, Jozsef...................................................... MoAT7.3 280 

.................................................................................. WeDT9 CC 

.................................................................................. WeDT9.8 3740 

Kraetzschmar, Gerhard ............................................ TuAT7.3 1030 

Kragic, Danica .......................................................... TuAT1.7 827 

.................................................................................. TuAT6.4 980 

.................................................................................. TuAT6.5 987 

.................................................................................. TuBT9 C 

.................................................................................. TuBT9.7 1554 

.................................................................................. TuBPT10.13 * 

.................................................................................. TuBPT10.14 * 

.................................................................................. WeAT2.2 2028 

.................................................................................. WeDT1.1 3342 

Kramer, Rebecca...................................................... MoAT9.8 414 

.................................................................................. TuCT9.2 1919 

Krauthausen, Peter................................................... ThCT5.4 4819 

.................................................................................. ThDPT10.13 * 

Kress-Gazit, Hadas .................................................. WeCT5 C 

.................................................................................. WeCT5 O 

.................................................................................. WeCT5.7 3120 

Kretzschmar, Henrik ................................................. MoAT2.6 86 

.................................................................................. MoBPT10.6 * 

.................................................................................. TuAT2.5 865

.................................................................................. TuBPT10.5 * 

Krid, Mohamed ......................................................... MoAT7.2 274 

Krieger, Kai............................................................... TuCT6.6 1789 

.................................................................................. WeAPT10.15 * 

Krishna, Madhava..................................................... ThBT3.5 4314 

.................................................................................. ThCPT10.8 * 

Kristan, Matej............................................................ WeDT1.8 3387 

Kroemer, Oliver ........................................................ TuBT9.6 1548 

.................................................................................. TuCPT10.24 * 

Kronander, Klas........................................................ MoBT8.4 710 

.................................................................................. TuAPT10.19 * 

Krontiris, Athanasios................................................. WeCT8.2 3235 

Kroschel, Kristian...................................................... TuBT1.1 1173 

Krug, Robert ............................................................. TuCT6.7 1797 

Krüger, Norbert......................................................... TuAT6.5 987 

.................................................................................. TuBPT10.14 * 

Kruijff, Geert-Jan ...................................................... WeDT1.8 3387 

Krupa, Alexandre...................................................... WeBT9.3 2831 

.................................................................................. WeBT9.4 2837 

.................................................................................. WeCPT10.25 * 

Krut, Sebastien......................................................... ThDT4.2 5067 

Kryczka, Przemyslaw ............................................... WeCT7.8 3221 

Kuan, Jiun-Yih .......................................................... MoAT9.1 372 

Kubus, Daniel ........................................................... WeBT2.4 2481 

.................................................................................. WeCPT10.4 * 

Kuchenbecker, Katherine J. ..................................... WeAT4 C 

.................................................................................. WeAT4 O 

.................................................................................. WeBT4 CC 

.................................................................................. WeBT4 O 

.................................................................................. WeCT4 O 

Kuehn, Benjamin ...................................................... TuBT1.1 1173 

Kuehne, Hildegard.................................................... ThCT5.4 4819 

.................................................................................. ThDPT10.13 * 

Kuemmerle, Rainer................................................... WeDT9.4 3716 

.................................................................................. ThAPT10.22 * 

Kuffner, James ......................................................... WeAT5.8 2199 

Kumar, Rajesh.......................................................... WeBT3.4 2539 

.................................................................................. WeCPT10.7 * 

Kumar, Vijay ............................................................. WeBT6.2 2668 

.................................................................................. WeCT5.8 3126 

.................................................................................. WeCT8.4 3248 

.................................................................................. WeDT8.5 3667 

.................................................................................. WeDPT10.22 * 

.................................................................................. ThAPT10.20 * 

Kumon, Makoto ........................................................ MoAT3.3 112 

Kuniyoshi, Yasuo...................................................... TuBT1.7 1214 

.................................................................................. TuBT8.7 1499 

Kunze, Lars .............................................................. WeCT7.1 3172 

Kunze, Mirko............................................................. TuCT6.6 1789 

.................................................................................. WeAPT10.15 * 

Kuperij, Nicole .......................................................... TuAT5.3 937 

Kurazume, Ryo......................................................... WeAT2.1 2020 

.................................................................................. ThBT1.5 4201 

.................................................................................. ThCPT10.2 * 

Kurdila, Andrew ........................................................ TuAT9.4 1140 

.................................................................................. TuBPT10.22 * 

Kurita, Yuichi ............................................................ WeAT4.3 2127 

Kurniawati, Hanna .................................................... WeDT6.3 3551 

Kushleyev, Aleksandr............................................... TuAT9.5 1146 

.................................................................................. TuBPT10.23 * 

Kuthirummal, Sujit .................................................... ThAT3.4 3874 

.................................................................................. ThBPT10.7 * 

Kuwahara, Hiroyuki .................................................. ThBT8.4 4550 

.................................................................................. ThCPT10.22 * 

Kuwata, Yoshiaki...................................................... ThCT3.3 4728 

Kwak, Kiho................................................................ WeCT9.1 3283 

Kweon, In So ............................................................ WeCT9.2 3290 

.................................................................................. ThBT6.3 4436 

Kwon, Dong-Soo ...................................................... WeBT7 C 

.................................................................................. WeBT7.8 2759 


–245– 

Kyrkjebø, Erik ........................................................... MoAT6.6 247 

.................................................................................. MoBPT10.18 * 

.................................................................................. ThCT2.2 

L 

4676 

Labbé, Mathieu......................................................... TuBT2.7 1271 

Lacerda, Bruno ......................................................... WeCT5.1 3081 

Ladjal, Hamid............................................................ TuBT3.8 1315 

Lakaemper, Rolf ....................................................... ThAT2.2 3808 

Lal, Sara ................................................................... ThBT9.7 4608 

Lalande, Viviane ....................................................... TuAT3.4 901 

.................................................................................. TuBT3.3 1285 

.................................................................................. TuBPT10.7 * 

Lallée, Stéphane....................................................... WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Lam, Tin Lun............................................................. TuBT8.5 1487 

.................................................................................. TuCPT10.20 * 

Lambercy, Olivier...................................................... WeCT4.8 3074 

Lambert, Ghislain ..................................................... TuCT7.7 1849 

Lamiraux, Florent...................................................... ThBT5.5 4408 

.................................................................................. ThCPT10.14 * 

Lampert, Christoph H. .............................................. MoAT8.3 332 

Lamy-Perbal, Sylvie.................................................. WeBT2.7 2505 

Lan, Chao-Chieh ...................................................... TuCT9.8 1962 

Lane, Ian................................................................... MoAT3 CC 

.................................................................................. MoAT3 O 

.................................................................................. MoBT3 O 

.................................................................................. MoBT3.1 * 

Langner, Tim ............................................................ WeBT1.5 2430 

Lapeyre, Matthieu..................................................... TuBT8.2 1465 

Lapierre, Lionel......................................................... WeDT6.4 3558 

Laschi, Cecilia .......................................................... ThCT5.5 4826 

.................................................................................. ThDPT10.14 * 

Latscha, Stella .......................................................... MoAT9.7 408 

Lau, Haye ................................................................. WeDT8.8 3686 

Laugier, Christian ..................................................... WeAT1.8 2014 

Laurent, Guillaume J. ............................................... ThDT6.2 5121 

LaValle, Steven M .................................................... WeAT5.8 2199 

.................................................................................. WeBT2.8 2511 

.................................................................................. WeBT5 CC 

.................................................................................. WeBT5 O 

.................................................................................. WeCT5.4 3101 

.................................................................................. WeDPT10.13 * 

.................................................................................. ThAT3.2 3862 

Lavoie, Samuel......................................................... TuCT7.7 1849 

Lawitzky, Martin........................................................ WeBT1.3 2416 

Lazea Gheorghe, Gheorghe..................................... TuBT2.5 1256 

.................................................................................. TuCPT10.5 * 

Lazewatsky, Daniel................................................... ThAT8.8 4115 

Le, Minh-Quyen ........................................................ MoBT7.4 659 

.................................................................................. MoBT9.1 738 

.................................................................................. TuAPT10.16 * 

Le Fort-Piat, Nadine ................................................. ThDT6.2 5121 

Lea, Colin ................................................................. ThAT2.3 3816 

Lebastard, Vincent.................................................... TuCT8.7 1901 

Lee, C. S. George..................................................... WeAT8 C 

.................................................................................. WeAT8.1 2300 

Lee, Daniel D............................................................ WeAT5.6 2186 

.................................................................................. WeBPT10.15 * 

.................................................................................. ThAT5.6 3963 

.................................................................................. ThBPT10.15 * 

Lee, Deukhee ........................................................... WeBT9.3 2831 

Lee, Dongheui .......................................................... MoAT8.5 344 

.................................................................................. MoBPT10.23 * 

.................................................................................. TuAT6.7 1002 

.................................................................................. WeBT1.3 2416 

.................................................................................. WeDT7.7 3626 

Lee, Dongjun ............................................................ MoAT4.3 149 

.................................................................................. WeCT4.3 3039 

Lee, Gim Hee ........................................................... TuCT2.6 1655 

.................................................................................. WeAPT10.6 *

.................................................................................. ThAT6.7 4012 

Lee, Jae Hoon .......................................................... WeDT7.2 3595 

Lee, James............................................................... ThAT2.3 3816 

Lee, Ji Yeong Lee..................................................... ThDT6.3 5127 

Lee, Jongwon ........................................................... WeBT3.2 2524 

Lee, Min Kyung......................................................... WeAT1.4 1986 

.................................................................................. WeBPT10.1 * 

Lee, Seong-Whan..................................................... WeCT4.3 3039 

Lee, Seongsoo ......................................................... TuAT2.1 841 

Lee, Seung Hwan..................................................... TuAT5.8 967 

Lee, Tae-kyeong....................................................... TuAT2.1 841 

Lee, Yi-Chiao............................................................ TuCT9.8 1962 

Leeper, Adam Eric.................................................... WeBT4.1 2570 

Legrand, Bernard...................................................... MoAT1.8 45 

Lehman, Joel............................................................ WeBT8.7 2802 

Lehnert, Christopher................................................. MoBT8.1 692 

Leishman, Robert ..................................................... WeBT6.5 2688 

.................................................................................. WeCPT10.17 * 

.................................................................................. ThDT8.2 5173 

Lemaignan, Séverin.................................................. WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Lemerle, Pierre......................................................... WeCT7.7 3213 

Lenain, Roland ......................................................... ThAT8.1 4072 

.................................................................................. ThBT9.1 4569 

Lenz, Alexander........................................................ WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Lenz, Claus............................................................... WeDT1.6 3375 

.................................................................................. ThAPT10.3 * 

Leon, Lisandro.......................................................... TuBT5.2 1321 

Leu, Adrian ............................................................... TuAT1.1 786 

Leutenegger, Stefan................................................. WeAT6.5 2223 

.................................................................................. WeBPT10.17 * 

Leven, Severin.......................................................... ThCT9.6 5015 

.................................................................................. ThDPT10.27 * 

Lewinger, William ..................................................... MoAT5.7 215 

Lewis, Bennie ........................................................... WeBT4.4 2590 

.................................................................................. WeCPT10.10 * 

Li, Baopu .................................................................. WeAT3.3 2090 

Li, Bin........................................................................ TuCT8.2 1869 

Li, Howard ................................................................ TuAT2.3 853 

.................................................................................. TuAT2.7 880 

Li, Jinglin................................................................... ThBT1.6 4207 

.................................................................................. ThCPT10.3 * 

Li, Peng .................................................................... ThBT1.8 4222 

Li, Xiaodong.............................................................. TuCT3.8 1705 

Li, Xiaofei.................................................................. WeCT1.3 2879 

Li, Xin........................................................................ MoAT4.8 184 

Li, Y.F. ...................................................................... WeDT2.8 3440 

Li, Yuanhong ............................................................ ThBT9.2 4575 

Li, Zhanchu............................................................... TuBT8.3 1473 

Liang, Dong .............................................................. MoBT3.8 550 

Liang, Jianhong ........................................................ TuCT8.1 1863 

Libby, Tom................................................................ TuCT8.5 1887 

.................................................................................. WeAPT10.20 * 

Lidholm, Jörgen........................................................ TuCT2.2 1626 

Liemhetcharat, Somchaya........................................ WeDT8.1 3638 

Lien, Jyh-Ming .......................................................... TuCT1 CC 

.................................................................................. TuCT1.2 1573 

.................................................................................. WeBT5.4 2626 

.................................................................................. WeCPT10.13 * 

Likhachev, Maxim..................................................... TuAT9.5 1146 

.................................................................................. TuBPT10.23 * 

.................................................................................. WeAT5 CC 

.................................................................................. WeAT5 O 

.................................................................................. WeCT8.5 3254 

.................................................................................. WeDT5 C 

.................................................................................. WeDT5 O 

.................................................................................. WeDPT10.23 * 

Lim, Hun-ok .............................................................. WeCT7.8 3221 

Lim, Hup Boon.......................................................... ThCT7.6 4923 


–246– 

.................................................................................. ThDPT10.21 * 

Lim, Jongwoo ........................................................... TuCT2.1 1618 

.................................................................................. ThAT6.3 3982 

Lima, Pedro .............................................................. WeCT5.1 3081 

Lin, Pei-Chun............................................................ TuBT8.6 1493 

.................................................................................. TuCPT10.21 * 

.................................................................................. ThDT4 CC 

Lin, Yun .................................................................... WeDT1.2 3349 

Lind, Morten.............................................................. WeBT9.8 2861 

Lindsey, Quentin....................................................... WeBT6.2 2668 

Lippiello, Vincenzo.................................................... ThBT1.4 4194 

.................................................................................. ThCPT10.1 * 

Lipson, Hod .............................................................. ThBT4.5 4366 

.................................................................................. ThCPT10.11 * 

Little, James J........................................................... WeBT9.2 2825 

.................................................................................. ThCT6.8 4885 

Liu, Albert ................................................................. TuCT3.3 1674 

Liu, Chao .................................................................. WeDT6.4 3558 

.................................................................................. ThAPT10.13 * 

Liu, Dikai................................................................... WeDT8.8 3686 

Liu, Hong .................................................................. WeCT1.3 2879 

Liu, Jindong .............................................................. MoBT3.7 542 

Liu, Jingmeng ........................................................... MoBT9.2 744 

Liu, Ming ................................................................... ThAT2.4 3824 

.................................................................................. ThBPT10.4 * 

Liu, Wen ................................................................... TuBT5.6 1346 

.................................................................................. TuCPT10.12 * 

Liu, Yen-Chen........................................................... MoBT7.7 679 

Liu, Ziyuan ................................................................ WeDT7.7 3626 

Lofaro, Daniel ........................................................... WeCT1.8 2916 

Loiselle, Stéphane .................................................... MoBT3.3 518 

Long, Jonathan......................................................... TuAT1.2 793 

Lopes, Ana ............................................................... WeBT1.6 2438 

.................................................................................. WeCPT10.3 * 

Lopes, Gabriel .......................................................... WeBT7.3 2729 

Low, K. H. ................................................................. MoAT5 O 

.................................................................................. MoBT5 C 

.................................................................................. MoBT5 O 

.................................................................................. MoBT5.5 568 

.................................................................................. TuCT5.6 1743 

.................................................................................. WeAPT10.12 * 

.................................................................................. ThCT7.6 4923 

.................................................................................. ThDPT10.21 * 

Lozano-Perez, Tomas .............................................. WeAT5.1 * 

Lu, Wenyu ................................................................ ThBT3.6 4321 

.................................................................................. ThCPT10.9 * 

Lu, Yan ..................................................................... ThAT8.3 4084 

Lu, Yanyan ............................................................... WeBT5.4 2626 

.................................................................................. WeCPT10.13 * 

Lu, Yibiao.................................................................. WeDT5.2 3507 

Lu, Zhiguo................................................................. ThDT5.2 5094 

Luber, Matthias......................................................... ThAT2.7 3844 

Lucas, Blake ............................................................. TuBT5.6 1346 

.................................................................................. TuCPT10.12 * 

Luettel, Thorsten....................................................... ThBT8.6 4562 

.................................................................................. ThCPT10.24 * 

Lumia, Ron ............................................................... WeAT8 CC 

.................................................................................. WeAT8.6 2333 

Luna, Ryan ............................................................... WeCT8.7 3268 

Luo, Guoliang ........................................................... WeAT2.2 2028 

Luo, Ren ................................................................... WeCT2.8 2965 

Lupashin, Sergei....................................................... ThDT6.1 5113 

.................................................................................. ThDT8.3 5179 

Luu, Trieu Phat ......................................................... ThCT7.6 4923 

.................................................................................. ThDPT10.21 * 

Ly, Olivier.................................................................. TuBT8 CC 

.................................................................................. TuBT8.2 

M 

1465 

Ma, Shugen .............................................................. TuCT8.2 1869 

.................................................................................. ThDT3 CC

.................................................................................. ThDT3.1 5035 

Ma, Yan .................................................................... ThCT8.6 4961 

.................................................................................. ThDPT10.24 * 

MacAllister, Brian...................................................... TuAT9.5 1146 

.................................................................................. TuBPT10.23 * 

Macasieb, Juan Antonio Miguel Lim......................... ThCT4.7 4797 

Macdonald, John ...................................................... ThDT8.2 5173 

MacLachlan, Robert A.............................................. ThDT7.4 5160 

Macwan, Ashish ....................................................... ThBT8.3 4544 

Madhavan, Raj ......................................................... ThDT6 C 

Mae, Yasushi............................................................ MoAT1.5 25 

.................................................................................. MoBT1.2 427 

.................................................................................. MoBPT10.2 * 

.................................................................................. TuAT1.4 807 

.................................................................................. TuBPT10.1 * 

Maeda, Guilherme Jorge.......................................... MoBT8.6 725 

.................................................................................. TuAPT10.21 * 

Magnenat, Stéphane ................................................ ThAT2.4 3824 

.................................................................................. ThBPT10.4 * 

Magnusson, Martin................................................... ThAT8.2 4078 

Mahmudi, Mentar...................................................... WeBT5.8 2653 

Mahnič, Marko.......................................................... WeDT1.8 3387 

Mahon, Ian................................................................ ThBT6.6 4455 

.................................................................................. ThCPT10.18 * 

Mahoor, Mohammad ................................................ WeAT9 CC 

.................................................................................. WeAT9.5 2377 

.................................................................................. WeBPT10.26 * 

Mair, Elmar ............................................................... WeCT9.3 3297 

Mairiaux, Estelle ....................................................... MoAT1.8 45 

Majdik, Andras.......................................................... TuBT2.5 1256 

.................................................................................. TuCPT10.5 * 

Majidi, Carmel........................................................... TuCT9.2 1919 

Makikawa, Masaaki .................................................. TuCT9.6 1946 

.................................................................................. WeAPT10.24 * 

Malosio, Matteo ........................................................ WeCT9.7 3327 

Malvezzi, Monica...................................................... TuAT6.8 1008 

Malysz, Pawel........................................................... MoBT7.2 645 

Mansard, Nicolas...................................................... ThAT3.6 3887 

.................................................................................. ThAT9.2 4127 

.................................................................................. ThBPT10.9 * 

Mansour, Darine....................................................... WeCT7.7 3213 

Manz, Michael .......................................................... ThBT8.6 4562 

.................................................................................. ThCPT10.24 * 

Marble, James.......................................................... ThBT3.2 4292 

Marchal-Crespo, Laura............................................. WeCT4.7 3068 

Marchand, Eric ......................................................... MoBT6.6 619 

.................................................................................. TuAPT10.15 * 

.................................................................................. ThCT8.1 4929 

Marchese, Andrew.................................................... MoBT9.4 756 

.................................................................................. TuAPT10.22 * 

Marchetti, Luca......................................................... WeBT8.6 2796 

.................................................................................. WeCPT10.24 * 

Marino, Alessandro................................................... WeBT8.3 2778 

Mariottini, Gian Luca................................................. WeAT6.6 2229 

.................................................................................. WeAT9.4 2371 

.................................................................................. WeBPT10.18 * 

.................................................................................. WeBPT10.25 * 

.................................................................................. WeDT7 C 

.................................................................................. WeDT7.4 3608 

.................................................................................. ThAPT10.16 * 

Marks, Tim K. ........................................................... ThCT2.4 4690 

.................................................................................. ThDPT10.4 * 

Marques, Hugo......................................................... TuAT7.8 1063 

Marques, Lino........................................................... ThBT2.8 4277 

Martel, Sylvain.......................................................... TuAT3 C 

.................................................................................. TuAT3 O 

.................................................................................. TuAT3.4 901 

.................................................................................. TuBT3 CC 

.................................................................................. TuBT3 O 

.................................................................................. TuBT3.3 1285 


–247– 

.................................................................................. TuBT3.6 1304 

.................................................................................. TuBPT10.7 * 

.................................................................................. TuCT3 C 

.................................................................................. TuCT3 O 

.................................................................................. TuCPT10.9 * 

Martin, Christian ....................................................... WeBT1.5 2430 

Martinelli, Agostino ................................................... WeBT2 CC 

.................................................................................. WeBT2.1 2460 

Martinet, Philippe...................................................... WeBT9.7 2855 

.................................................................................. ThAT8.1 4072 

Martinoli, Alcherio ..................................................... WeBT8.1 2765 

.................................................................................. WeCT8.3 3241 

.................................................................................. WeCT9.5 3313 

.................................................................................. WeDPT10.26 * 

.................................................................................. ThBT4.1 4341 

Martinson, Eric.......................................................... MoAT3.5 125 

.................................................................................. MoBPT10.8 * 

Marton, Zoltan-Csaba............................................... ThBT2.6 4263 

.................................................................................. ThCPT10.6 * 

Maruyama, Hisataka................................................. MoAT1.3 13 

Mascaro, Stephen .................................................... MoAT9 CC 

.................................................................................. MoAT9.6 402 

.................................................................................. MoBT9.8 780 

.................................................................................. MoBPT10.27 * 

.................................................................................. WeBT4.5 2596 

.................................................................................. WeCPT10.11 * 

Masehian, Ellips ....................................................... ThBT3.3 4299 

Mason, Matthew T. ................................................... TuCT6.8 1804 

.................................................................................. ThAT3.1 3856 

Masone, Carlo .......................................................... WeAT6.4 2215 

.................................................................................. WeBPT10.16 * 

Mastrangeli, Massimo............................................... ThBT4.1 4341 

Mastrogiovanni, Fulvio.............................................. WeDT9.1 3694 

Masuda, Taisuke ...................................................... MoAT1.3 13 

Mataric, Maja ............................................................ WeBT1.1 2403 

Mathews, Nithin ........................................................ ThCT4.2 4762 

.................................................................................. ThCT9.8 5027 

Mathieu, Philippe ...................................................... WeDT5.4 3519 

.................................................................................. ThAPT10.10 * 

Mathisen, Geir .......................................................... WeBT9.8 2861 

Matos, Vítor .............................................................. WeAT7.7 2286 

Matsubara, Takamitsu .............................................. ThAT5.8 3975 

Matsuno, Fumitoshi .................................................. TuCT8 CC 

.................................................................................. TuCT8.8 1907 

Matsusaka, Yosuke .................................................. WeAT1.6 2000 

.................................................................................. WeBPT10.3 * 

Mattos, Leonardo...................................................... TuBT5.8 1359 

Maughan, Thom ....................................................... WeCT6.3 3132 

Mavroidis, Constantinos ........................................... ThCT7 CC 

.................................................................................. ThCT7.1 4893 

Maxwell, Bruce ......................................................... ThAT8.8 4115 

Maycock, Jonathan................................................... WeCT2.5 2947 

.................................................................................. WeDPT10.5 * 

Mazalaigue, Stéphane.............................................. WeBT3.3 2532 

McAllester, David...................................................... ThBT6.4 4442 

.................................................................................. ThCPT10.16 * 

McBride, James........................................................ ThBT4.7 4378 

McCann, Mike........................................................... WeCT6.3 3132 

McGinnity, Martin...................................................... WeDT3.1 3446 

.................................................................................. ThCT2.8 4716 

McLain, T.W. ............................................................ WeBT6.5 2688 

.................................................................................. WeCPT10.17 * 

.................................................................................. ThDT8.2 5173 

McLaren, David ........................................................ WeCT6.4 3140 

.................................................................................. WeDPT10.16 * 

McPherson, Timothy................................................. TuCT9.5 1940 

.................................................................................. WeAPT10.23 * 

Medina Ayala, Ana Ivonne........................................ WeCT5.5 3108 

.................................................................................. WeDPT10.14 * 

Medina Hernandez, Jose Ramon ............................. WeBT1.3 2416

Medioni, Gerard........................................................ ThAT2.1 3800 

Medrano-Cerda, Gustavo......................................... TuAT8.5 1093 

.................................................................................. TuBPT10.20 * 

Meek, Sanford .......................................................... TuCT9 CC 

.................................................................................. TuCT9.3 1927 

.................................................................................. WeDT7.8 3632 

.................................................................................. ThBT1.1 4174 

Meeussen, Wim........................................................ WeCT2 CC 

.................................................................................. WeCT2.4 2941 

.................................................................................. WeDPT10.4 * 

Mefoued, Saber........................................................ TuCT5.7 1749 

Meger, David Paul.................................................... ThCT6.8 4885 

Meghjani, Malika....................................................... ThDT3.3 5048 

Mehnert, Jens........................................................... ThBT9.4 4587 

.................................................................................. ThCPT10.25 * 

Mehrjerdi, Hasan ...................................................... WeAT8.7 2340 

Meier, Franziska....................................................... WeDT2.3 3407 

Meilland, Maxime...................................................... ThBT2.3 4242 

Meirion-Griffith, Gareth............................................. MoAT7.7 305 

Meißner, Pascal........................................................ ThCT1.3 4633 

Melchiorri, Claudio.................................................... TuCT6.4 1775 

.................................................................................. WeAPT10.13 * 

.................................................................................. ThAT3.8 3899 

Melendez-Calderon, Alejandro................................. WeBT4.2 2578 

Melhuish, Chris......................................................... WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Mellinger, Daniel....................................................... WeBT6.2 2668 

Menegatti, Emanuele................................................ WeDT3.6 3480 

.................................................................................. ThAPT10.9 * 

Meng, Max Q.-H. ...................................................... WeAT3.3 2090 

Mermoud, Gregory ................................................... ThBT4.1 4341 

Merten, Matthias....................................................... WeBT1.5 2430 

Merz, Torsten ........................................................... ThCT8.5 4953 

.................................................................................. ThDPT10.23 * 

Messié, Monique ...................................................... WeCT6.3 3132 

Metta, Giorgio........................................................... TuBT9.3 1526 

.................................................................................. WeCT1.5 2895 

.................................................................................. WeDT9 C 

.................................................................................. WeDT9.1 3694 

.................................................................................. WeDPT10.2 * 

.................................................................................. ThAT9.6 4154 

.................................................................................. ThAT9.7 4161 

.................................................................................. ThBPT10.27 * 

Mettin, Uwe............................................................... ThDT5.2 5094 

Mettler, Bernie .......................................................... WeCT5.3 3093 

Metzger, Jean-Claude .............................................. WeCT4.8 3074 

Meyer-Delius, Daniel ................................................ WeCT8.8 3276 

Micaelli, Alain............................................................ WeCT7.7 3213 

Michael, Nathan........................................................ WeAT6 C 

.................................................................................. WeAT6 O 

.................................................................................. WeBT6 O 

.................................................................................. WeBT6.8 2708 

.................................................................................. WeDT8.5 3667 

.................................................................................. ThAPT10.20 * 

Michaud, Francois .................................................... TuBT2.7 1271 

Michel, Fabien .......................................................... ThDT3.2 5041 

Michelin, Micaël........................................................ WeBT9.7 2855 

Mier-Y-Teran-Romero, Luis...................................... ThAT4.3 3905 

Mignano, Anthony..................................................... MoBT5.7 580 

Milella, Annalisa........................................................ MoAT6.7 255 

Milighetti, Giulio ........................................................ WeCT7.4 3192 

.................................................................................. WeDPT10.19 * 

Miller, Stephen.......................................................... ThCT6.7 4877 

Milutinovic, Dejan ..................................................... ThAT4.5 3917 

.................................................................................. ThBT4 CC 

.................................................................................. ThBPT10.11 * 

Minamizawa, Kouta .................................................. MoAT4.4 157 

.................................................................................. MoBPT10.10 * 

Minato, Takashi ........................................................ WeDT3.6 3480 

.................................................................................. ThAPT10.9 * 


–248– 

Ming, Aiguo............................................................... TuCT9.7 1954 

.................................................................................. ThAT7.4 4048 

.................................................................................. ThCT3.4 4735 

.................................................................................. ThDPT10.7 * 

Minor, Mark............................................................... MoAT7.5 292 

.................................................................................. MoBPT10.20 * 

.................................................................................. ThCT8 CC 

.................................................................................. ThCT8.8 4975 

Miroir, Mathieu.......................................................... WeBT3.3 2532 

Misra, Sarthak .......................................................... MoBT1.1 421 

.................................................................................. TuAT5.3 937 

.................................................................................. WeAT3.1 2076 

.................................................................................. WeBT3 CC 

.................................................................................. WeBT3.7 2557 

Mita, Seiichi .............................................................. ThBT6.4 4442 

.................................................................................. ThCPT10.16 * 

Mitra, Urbashi ........................................................... WeDT6.5 3564 

.................................................................................. ThAPT10.14 * 

Mitsuhashi, Masaru .................................................. WeCT7.2 3179 

Miura, Hiroki ............................................................. MoBT3.4 524 

.................................................................................. TuAPT10.7 * 

Miura, Kanako .......................................................... ThBT5.3 4392 

.................................................................................. ThBT5.8 4428 

Miyamori, Go ............................................................ ThBT5.4 4400 

.................................................................................. ThCPT10.13 * 

Miyashita, Takahiro .................................................. WeDT7.1 3589 

Miyazaki, Fumio........................................................ ThBT7.4 4496 

.................................................................................. ThCPT10.19 * 

Mizumoto, Takeshi ................................................... MoAT3.4 118 

.................................................................................. MoBPT10.7 * 

.................................................................................. WeCT1.7 2910 

Mobarhani, Amir ....................................................... TuAT9.2 1128 

Mochiyama, Hiromi................................................... ThCT1.6 4652 

.................................................................................. ThDPT10.3 * 

Mohammadi, Mahmood............................................ TuBT3.6 1304 

.................................................................................. TuCPT10.9 * 

Mohammed, Samer .................................................. TuCT5.7 1749 

Mohan, Anush .......................................................... ThBT4.7 4378 

Mohta, Kartik ............................................................ WeBT6.8 2708 

Mohtat, Arash ........................................................... WeDT9.8 3740 

Molinari Tosatti, Lorenzo .......................................... WeCT9.7 3327 

Molotchnikoff, Stéphane ........................................... MoBT3.3 518 

Mombaur, Katja ........................................................ ThBT5.1 * 

Mondada, Francesco................................................ ThCT4.2 4762 

.................................................................................. ThCT9.1 4981 

Montano, Luis ........................................................... WeCT1.4 2887 

.................................................................................. WeDPT10.1 * 

Montés Sánchez, Nicolás ......................................... TuCT1.1 1567 

Montesano, Luis ....................................................... ThBT6.5 4448 

.................................................................................. ThCPT10.17 * 

Montiel, J.M.M .......................................................... TuBT2.8 1277 

Moon, AJung ............................................................ WeAT1.5 1994 

.................................................................................. WeBPT10.2 * 

Moon, Hyungpil......................................................... TuBT7.7 1447 

Moore, Joseph.......................................................... WeBT6.7 2700 

Moore, Richard James Donald ................................. ThCT8.2 4935 

Mora, Marta Covadonga........................................... TuCT1.1 1567 

Moral del Agua, Diego .............................................. ThBT7.8 4522 

Morbidi, Fabio........................................................... WeAT6.6 2229 

.................................................................................. WeBPT10.18 * 

.................................................................................. WeDT7.4 3608 

.................................................................................. ThAPT10.16 * 

Moreau, Richard ....................................................... MoBT7.4 659 

.................................................................................. TuAPT10.16 * 

Morel, Guillaume ...................................................... TuBT5.4 1333 

.................................................................................. TuBT5.5 1339 

.................................................................................. TuCPT10.10 * 

.................................................................................. TuCPT10.11 * 

Mori, Taketoshi ......................................................... WeDT2.5 3419 

.................................................................................. WeDT7.3 3601

.................................................................................. ThAPT10.5 * 

Morimoto, Jun........................................................... WeBT7.1 2715 

.................................................................................. WeCT7.3 3185 

.................................................................................. ThAT5.8 3975 

Morin, Pascal............................................................ WeCT9.8 3335 

Morioka, Hiroshi........................................................ ThAT6.5 3998 

.................................................................................. ThBPT10.17 * 

Morisawa, Mitsuharu ................................................ ThBT5.3 4392 

.................................................................................. ThBT5.4 4400 

.................................................................................. ThBT5.8 4428 

.................................................................................. ThCPT10.13 * 

Morozovsky, Nicholas............................................... WeBT7.5 2741 

.................................................................................. WeCPT10.20 * 

.................................................................................. ThAT8.5 4097 

.................................................................................. ThBPT10.23 * 

Morris, Aaron............................................................ ThAT2.3 3816 

Mörtl, Alexander ....................................................... WeBT1.3 2416 

Morton, Ryan............................................................ TuCT1.3 1579 

Mörwald, Thomas..................................................... TuAT1.5 813 

.................................................................................. TuBPT10.2 * 

Mösenlechner, Lorenz.............................................. ThAT9.4 4141 

.................................................................................. ThBPT10.25 * 

Motegi, Yuichi........................................................... ThCT9.7 5021 

Mouret, Jean-Baptiste .............................................. ThBT4.4 4360 

.................................................................................. ThCPT10.10 * 

Mourikis, Anastasios................................................. MoAT2.3 65 

Moutinho, Nuno ........................................................ ThCT5.5 4826 

.................................................................................. ThDPT10.14 * 

Movafaghpour, Mohamad Ali.................................... ThBT3.3 4299 

Mueller, Joerg........................................................... ThCT8.3 4941 

Mueller, Steffen ........................................................ WeBT1.5 2430 

Muelling, Katharina................................................... MoAT8.3 332 

Mukai, Toshiharu...................................................... WeBT1.7 2445 

Mukherjee, Ranjan ................................................... ThCT3.6 4749 

.................................................................................. ThDPT10.9 * 

Müller, Mark Wilfried................................................. ThDT6.1 5113 

Muñoz, Victor............................................................ WeAT3.8 2121 

Muñoz Hernandez, Laura Elena............................... WeBT6.4 2682 

.................................................................................. WeCPT10.16 * 

Muntwyler, Simon..................................................... TuAT3.7 919 

Murakami, Kazunori.................................................. ThBT9.5 4593 

.................................................................................. ThCPT10.26 * 

Murasaki, Kazuhiko .................................................. WeDT7.3 3601 

Murillo, Ana Cristina ................................................. WeAT9.6 2383 

.................................................................................. WeBPT10.27 * 

Murphy, Elizabeth..................................................... ThAT3.3 3868 

Murphy, Robin .......................................................... MoAT5.6 209 

Murray, Richard........................................................ WeAT2.6 2056 

.................................................................................. WeBPT10.6 * 

.................................................................................. WeCT3.8 3017 

Murrieta-Cid, Rafael ................................................. ThBT8.1 4528 

Muscio, Giuseppe..................................................... ThBT1.4 4194 

.................................................................................. ThCPT10.1 

N 

* 

Naba, Shu................................................................. TuCT7.6 1843 

.................................................................................. WeAPT10.18 * 

Nadeau, Caroline...................................................... WeBT9.4 2837 

.................................................................................. WeCPT10.25 * 

Nagai, Takayuki........................................................ TuBT9.2 1520 

.................................................................................. TuBT9.5 1540 

.................................................................................. TuCPT10.23 * 

Nagasaka, Tomomi .................................................. TuAT2.6 872 

.................................................................................. TuBPT10.6 * 

Nagatani, Keiji .......................................................... MoAT6.1 * 

.................................................................................. MoBT6.3 601 

Nagpal, Radhika....................................................... ThAT4.6 3923 

.................................................................................. ThBT7.3 4488 

.................................................................................. ThBPT10.12 * 

.................................................................................. ThCT9 CC 

.................................................................................. ThCT9.2 4989 


–249– 

Nakadai, Kazuhiro .................................................... MoAT3 O 

.................................................................................. MoAT3.2 106 

.................................................................................. MoAT3.6 131 

.................................................................................. MoAT3.8 143 

.................................................................................. MoBT3 C 

.................................................................................. MoBT3 O 

.................................................................................. MoBT3.4 524 

.................................................................................. MoBT3.5 530 

.................................................................................. MoBPT10.9 * 

.................................................................................. TuAPT10.7 * 

.................................................................................. TuAPT10.8 * 

Nakajima, Hirofumi ................................................... MoAT3.2 106 

.................................................................................. MoAT3.6 131 

.................................................................................. MoBT3.5 530 

.................................................................................. MoBPT10.9 * 

.................................................................................. TuAPT10.8 * 

Nakajima, Masahiro.................................................. MoAT1.1 1 

.................................................................................. MoBT1.3 433 

.................................................................................. MoBT1.7 457 

.................................................................................. TuCT3.6 1693 

.................................................................................. WeAPT10.9 * 

Nakamura, Keisuke .................................................. MoAT3.2 106 

.................................................................................. MoAT3.6 131 

.................................................................................. MoAT3.8 143 

.................................................................................. MoBT3.4 524 

.................................................................................. MoBPT10.9 * 

.................................................................................. TuAPT10.7 * 

Nakamura, Kenichi ................................................... ThBT9.5 4593 

.................................................................................. ThCPT10.26 * 

Nakamura, Tomoaki ................................................. TuBT9.2 1520 

.................................................................................. TuBT9.5 1540 

.................................................................................. TuCPT10.23 * 

Nakamura, Yoshihiko ............................................... MoAT7.1 268 

.................................................................................. WeDT2.2 3401 

.................................................................................. WeDT9.2 3701 

Nakamura, Yutaka.................................................... WeCT1.1 2867 

Nakanishi, Hiroki....................................................... MoBT6.7 625 

Nakanishi, Jun .......................................................... MoBT8 C 

.................................................................................. MoBT8.5 718 

.................................................................................. TuAPT10.20 * 

Nakano, Mikio........................................................... TuBT9.5 1540 

.................................................................................. TuCPT10.23 * 

Nakano, Tomoyasu .................................................. WeAT1.6 2000 

.................................................................................. WeBPT10.3 * 

Nakaoka, Shin'ichiro................................................. WeAT1.6 2000 

.................................................................................. WeBPT10.3 * 

.................................................................................. ThBT5.3 4392 

Nakashima, Hiromichi............................................... WeBT1.7 2445 

Nambi, Manikantan................................................... MoBT1.5 445 

.................................................................................. TuAPT10.2 * 

Naniwa, Keisuke....................................................... TuCT7.2 1817 

Narasimhan, Srinivasa ............................................. WeAT9.1 2352 

Nardi, Daniele........................................................... WeBT8.6 2796 

.................................................................................. WeCPT10.24 * 

Narioka, Kenichi ....................................................... ThCT5.7 4838 

Natale, Lorenzo ........................................................ TuBT9.3 1526 

.................................................................................. WeCT1.5 2895 

.................................................................................. WeDT9.1 3694 

.................................................................................. WeDPT10.2 * 

.................................................................................. ThAT9.6 4154 

.................................................................................. ThAT9.7 4161 

.................................................................................. ThBPT10.27 * 

Natarajan, Saravana K. ............................................ TuAT1.1 786 

Natraj, Ashutosh ....................................................... ThAT6.6 4006 

.................................................................................. ThBPT10.18 * 

Navarro-Alarcon, David ............................................ ThBT1.8 4222 

Nazari, Shaghayegh ................................................. TuAT9.2 1128 

Nebot, Eduardo ........................................................ ThBT9.6 4601 

.................................................................................. ThCPT10.27 * 

Negoro, Makoto ........................................................ MoBT1.4 439

.................................................................................. TuAPT10.1 * 

Neira, José ............................................................... ThAT6 C 

.................................................................................. ThAT6 O 

.................................................................................. ThBT6 C 

.................................................................................. ThBT6 O 

.................................................................................. ThCT6 CC 

Nejat, Goldie............................................................. ThBT8.3 4544 

Nelson, Bradley J. .................................................... TuAT3.7 919 

.................................................................................. TuBT3.5 1297 

.................................................................................. TuCPT10.8 * 

Nelson, Gabriel......................................................... MoBT5.1 * 

Nemec, Bojan ........................................................... ThCT5.6 4832 

.................................................................................. ThDPT10.15 * 

Nenchev, Dragomir................................................... WeCT7.2 3179 

Nerurkar, Esha ......................................................... MoBT2.6 502 

.................................................................................. TuAPT10.6 * 

Nettleton, Eric........................................................... ThBT2.2 4236 

Newman, Paul .......................................................... ThAT3.3 3868 

.................................................................................. ThAT6 O 

.................................................................................. ThBT6 O 

Nguyen, Binh............................................................ TuBT7.5 1433 

.................................................................................. TuCPT10.17 * 

Nguyen, Chanh-Nghiem........................................... MoBT1.2 427 

Nguyen, Kien Cuong ................................................ TuBT8.1 1459 

Nguyen, Yann........................................................... WeBT3.3 2532 

Nguyen-Tuong, Duy ................................................. MoBT8.2 698 

.................................................................................. MoBT8.3 704 

Nia Kosari, Sina........................................................ WeBT4.8 2614 

Nickl, Mathias ........................................................... TuAT7.7 1055 

Nieto, Juan ............................................................... MoAT2.7 92 

.................................................................................. ThBT9 CC 

.................................................................................. ThBT9.6 4601 

.................................................................................. ThCPT10.27 * 

Nieto-Granda, Carlos................................................ TuBT2.6 1264 

.................................................................................. TuCPT10.6 * 

Niiyama, Ryuma ....................................................... TuBT8.7 1499 

Nikolic, Janosch........................................................ WeBT6.6 2694 

.................................................................................. WeCPT10.18 * 

Nili Ahmadabadi, Majid............................................. WeBT7.2 2723 

Nill, Scott T. .............................................................. WeBT3.1 2517 

Nishii, Tatsuya.......................................................... WeCT7.2 3179 

Nishikawa, Satoshi ................................................... TuBT8.7 1499 

Noda, Tomoyuki ....................................................... ThAT5.8 3975 

Noda, Yoshitaka ....................................................... MoAT3.3 112 

Nogawa, Kousuke .................................................... TuCT3.6 1693 

.................................................................................. WeAPT10.9 * 

Noguchi, Hiroshi ....................................................... WeDT2.5 3419 

.................................................................................. ThAPT10.5 * 

Noh, Si Tae............................................................... WeDT7.2 3595 

Noonan, David.......................................................... TuAT5.5 949 

.................................................................................. TuBPT10.11 * 

Nori, Francesco ........................................................ WeDT9.1 3694 

.................................................................................. ThAT9.6 4154 

.................................................................................. ThAT9.7 4161 

.................................................................................. ThBT4.3 4354 

.................................................................................. ThBPT10.27 * 

Nothhelfer, Alexander............................................... TuAT7.7 1055 

Nunes, Urbano ......................................................... WeBT1 C 

.................................................................................. WeBT1.6 2438 

.................................................................................. WeCPT10.3 * 

Nuske, Stephen........................................................ MoAT6.3 227 

.................................................................................. WeAT9.1 2352 

.................................................................................. ThCT8.4 4947 

.................................................................................. ThDPT10.22 

O 

* 

O'Dowd, Paul Jason ................................................. ThCT9.3 4995 

O'Grady, Rehan........................................................ ThCT4.2 4762 

.................................................................................. ThCT9.8 5027 

O'Malley, Marcia....................................................... TuCT5.1 1711 

.................................................................................. WeAT4 CC 


–250– 

.................................................................................. WeAT4 O 

.................................................................................. WeBT4 O 

.................................................................................. WeCT4 C 

.................................................................................. WeCT4 O 

Obara, Takeshi ......................................................... MoAT1.2 7 

Odedra, Siddharth .................................................... MoAT7.4 286 

.................................................................................. MoBPT10.19 * 

Odhner, Lael............................................................. TuBT7.3 1420 

Ogasawara, Tsukasa................................................ WeAT4.3 2127 

Ogata, Tetsuya ......................................................... MoAT3.4 118 

.................................................................................. MoBPT10.7 * 

.................................................................................. WeCT1.7 2910 

Ogawa, Keita ............................................................ ThCT5.7 4838 

Oh, Paul Y. ............................................................... TuAT7.2 1022 

.................................................................................. WeCT1.8 2916 

Oh, Sang-Rok........................................................... ThBT7.2 4483 

Oh, Se Min................................................................ TuAT5.8 967 

Oh, Se-Young........................................................... TuAT2.1 841 

Ohara, Kenichi.......................................................... MoAT1.5 25 

.................................................................................. MoBT1.2 427 

.................................................................................. MoBPT10.2 * 

.................................................................................. TuAT1.4 807 

.................................................................................. TuBPT10.1 * 

Ohashi, Shigeo ......................................................... TuBT3.7 1309 

Oishi, Shuji ............................................................... WeAT2.1 2020 

Ok, Sungsuk ............................................................. MoAT7.1 268 

Okada, Masafumi ..................................................... MoBT9.5 762 

.................................................................................. TuAPT10.23 * 

.................................................................................. WeDT8.6 3673 

.................................................................................. ThAPT10.21 * 

.................................................................................. ThCT9.7 5021 

Okada, Shima........................................................... TuCT9.6 1946 

.................................................................................. WeAPT10.24 * 

Okamoto, Jun ........................................................... MoBT9.3 750 

Okamoto, Shogo....................................................... WeCT4.6 3060 

.................................................................................. WeDPT10.12 * 

Okamura, Allison M. ................................................. WeBT4.3 2584 

.................................................................................. ThDT7 CC 

Oki, Tomohisa .......................................................... MoBT6.7 625 

Okuno, Hiroshi G. ..................................................... MoAT3 O 

.................................................................................. MoAT3.1 * 

.................................................................................. MoAT3.4 118 

.................................................................................. MoBT3 CC 

.................................................................................. MoBT3 O 

.................................................................................. MoBPT10.7 * 

.................................................................................. WeCT1.7 2910 

Okuno, Keisuke ........................................................ WeDT1.7 3381 

Okutomi, Masatoshi.................................................. ThBT6.7 4463 

Oliveira, Miguel......................................................... WeAT7.7 2286 

Oliver, Gabriel A. ...................................................... WeDT6.7 3577 

Olmi, Roberto ........................................................... ThBT9.8 4615 

Olofsson, Bjorn ......................................................... ThCT2.6 4704 

.................................................................................. ThDPT10.6 * 

Olson, Edwin ............................................................ TuCT1.3 1579 

.................................................................................. ThAT3 C 

.................................................................................. ThAT3.5 3881 

.................................................................................. ThBT2.7 4271 

.................................................................................. ThBPT10.8 * 

Omer, Aiman ............................................................ WeCT7.8 3221 

Omura, Suguru ......................................................... MoAT9.5 395 

.................................................................................. MoBPT10.26 * 

Onal, Cagdas Denizel............................................... MoBT9 C 

.................................................................................. MoBT9.4 756 

.................................................................................. TuAPT10.22 * 

Ong, Lee-Ling Sharon .............................................. ThAT4.7 3931 

Oriolo, Giuseppe....................................................... MoBT2 C 

.................................................................................. MoBT2.1 469 

Osswald, Marc.......................................................... ThDT5.4 5107 

Osswald, Stefan ....................................................... ThCT5.8 4844 

Oster, Stéphane ....................................................... MoAT1.7 39

Osuka, Koichi ........................................................... TuCT7.2 1817 

Ota, Jun.................................................................... WeAT8.3 2314 

.................................................................................. ThCT2 C 

.................................................................................. ThCT2.5 4698 

.................................................................................. ThDPT10.5 * 

Otsuka, Takuma ....................................................... MoAT3.4 118 

.................................................................................. MoBPT10.7 * 

Ott, Christian............................................................. MoBT7.5 665 

.................................................................................. TuAPT10.17 * 

.................................................................................. ThBT5.7 4420 

Oudeyer, Pierre-Yves............................................... TuBT8.2 1465 

Overholt, Jim ............................................................ ThAT8.4 4091 

.................................................................................. ThBPT10.22 * 

Özkil, Ali Gürcan....................................................... TuAT2.2 847 

O'Reilly, Tom ............................................................ WeCT6.3 

P 

3132 

Paanajärvi, Janne Olavi............................................ MoAT2.2 59 

Padoy, Nicolas.......................................................... WeAT3.5 2102 

.................................................................................. WeBPT10.8 * 

Pagac, Daniel ........................................................... WeDT8.8 3686 

Pahlajani, Chetan ..................................................... ThAT4.4 3911 

.................................................................................. ThBPT10.10 * 

Paik, Jamie ............................................................... MoAT9.8 414 

Palli, Gianluca........................................................... TuCT6.4 1775 

.................................................................................. WeAPT10.13 * 

Pallottino, Lucia ........................................................ WeBT9.1 2817 

Palyart Lamarche, Jean Christophe ......................... ThAT5.4 3951 

.................................................................................. ThBPT10.13 * 

Pane, Salvador......................................................... TuBT3.5 1297 

.................................................................................. TuCPT10.8 * 

Pangercic, Dejan ...................................................... ThBT2.6 4263 

.................................................................................. ThCPT10.6 * 

Papadopoulos, Evangelos........................................ MoBT6.2 595 

.................................................................................. WeCT2 C 

.................................................................................. WeCT2.1 2922 

Papadopoulos, Georgios.......................................... WeDT6.3 3551 

Papanikolopoulos, Nikos .......................................... TuAT9.8 1167 

Park, Jaesik.............................................................. WeCT9.2 3290 

Park, Myoung Soo.................................................... ThBT7.2 4483 

Park, Yong-Lae......................................................... ThBT7.3 4488 

Parker, Chris............................................................. WeAT1.5 1994 

.................................................................................. WeBPT10.2 * 

.................................................................................. WeDT1.4 3361 

.................................................................................. ThAPT10.1 * 

Parker, Lynne ........................................................... WeAT2 C 

.................................................................................. WeAT2.4 2044 

.................................................................................. WeBPT10.4 * 

Parks, Perry.............................................................. MoBT5.6 574 

.................................................................................. TuAPT10.12 * 

Parnandi, Avinash .................................................... WeBT1.1 2403 

Parra Vega, Vicente ................................................. TuCT1.8 1612 

Parusel, Sven ........................................................... WeDT1.5 3367 

.................................................................................. ThAPT10.2 * 

Pascoal, Antonio....................................................... WeCT6.8 3166 

Pashkevich, Anatol................................................... ThAT7.2 4034 

Paskarbeit, Jan......................................................... ThAT1.6 3776 

.................................................................................. ThBPT10.3 * 

Passaglia, Andrea .................................................... ThAT1.5 3770 

.................................................................................. ThBPT10.2 * 

Passig, Georg........................................................... WeAT3.6 2108 

.................................................................................. WeBPT10.9 * 

Pastor, Peter............................................................. MoAT8.2 325 

.................................................................................. MoAT8.8 365 

.................................................................................. ThCT1.4 4639 

.................................................................................. ThDPT10.1 * 

Patel, Rajnikant V..................................................... MoBT7.3 653 

.................................................................................. WeBT3.6 2551 

.................................................................................. WeCPT10.9 * 

Patoglu, Volkan ........................................................ ThCT7.5 4917 

.................................................................................. ThDPT10.20 * 


–251– 

Patrikalakis, Nicholas ............................................... WeDT6.3 3551 

Pattacini, Ugo ........................................................... WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

.................................................................................. ThAT9.6 4154 

.................................................................................. ThBPT10.27 * 

Paull, Liam................................................................ TuAT2.3 853 

.................................................................................. TuAT2.7 880 

Paulus, Dietrich ........................................................ WeDT5.8 3545 

Payandeh, Shahram................................................. WeAT3.4 2096 

.................................................................................. WeBPT10.7 * 

Payne, Christopher................................................... TuAT5.5 949 

.................................................................................. TuBPT10.11 * 

Pchelkin, Stepan....................................................... ThDT5.2 5094 

Pedrocchi, Nicola...................................................... WeCT9.7 3327 

Pedrono, Brice.......................................................... WeBT4.2 2578 

Peer, Angelika .......................................................... MoAT4 C 

.................................................................................. MoAT4 O 

.................................................................................. TuAT6.7 1002 

Pehlivan, Utku .......................................................... TuCT5.1 1711 

Peng, Huei................................................................ WeCT3.6 3004 

.................................................................................. WeDPT10.9 * 

Peng, Peter............................................................... WeBT3.4 2539 

Penning, Ryan .......................................................... ThDT7.1 5139 

Peralta Cabezas, José Luis...................................... WeAT7.2 2255 

Perdereau, Véronique .............................................. TuBT8.1 1459 

Pereira, Flávio Garcia............................................... WeDT7.6 3620 

.................................................................................. ThAPT10.18 * 

Perez, Alejandro ....................................................... ThBT3.4 4307 

.................................................................................. ThCPT10.7 * 

Pérez Arias, Antonia................................................. WeCT4.5 3053 

.................................................................................. WeDPT10.11 * 

Perez-Arancibia, Nestor O........................................ TuAT8.7 1107 

Pericet-Camara, Ramon........................................... TuCT9.1 1913 

Perlmutter, Leah ....................................................... ThAT8.8 4115 

Perrin, Nicolas Yves ................................................. ThBT5.5 4408 

.................................................................................. ThCPT10.14 * 

Persson, Erik ............................................................ MoAT6.4 235 

.................................................................................. MoBPT10.16 * 

Peterlik, Igor ............................................................. WeBT4.7 2608 

Peters, Jan ............................................................... MoAT8 C 

.................................................................................. MoAT8.3 332 

.................................................................................. MoAT8.4 338 

.................................................................................. MoBT8.2 698 

.................................................................................. MoBT8.3 704 

.................................................................................. MoBPT10.22 * 

.................................................................................. TuBT9.6 1548 

.................................................................................. TuCPT10.24 * 

Peters, Steven .......................................................... MoAT7.6 298 

.................................................................................. MoBPT10.21 * 

Peterson, Gilbert....................................................... TuAT2.4 859 

.................................................................................. TuBPT10.4 * 

Peterson, Kevin ........................................................ ThDT4.4 5080 

Petit, Antoine ............................................................ MoBT6.6 619 

.................................................................................. TuAPT10.15 * 

Petit, Florian ............................................................. TuCT7.5 1836 

.................................................................................. WeAPT10.17 * 

.................................................................................. ThBT1.2 4180 

Pêtrès, Clément........................................................ WeDT6.6 3571 

.................................................................................. ThAPT10.15 * 

Petric, Tadej ............................................................. ThCT5.6 4832 

.................................................................................. ThDPT10.15 * 

Petrisor, Doru ........................................................... TuAT5.4 943 

.................................................................................. TuBPT10.10 * 

Petruska, Andrew ..................................................... WeDT7.8 3632 

Petsch, Susanne ...................................................... TuBT1.8 1221 

Pettersen, Kristin Y................................................... MoAT6.6 247 

.................................................................................. MoBPT10.18 * 

.................................................................................. ThCT2.2 4676 

Peynot, Thierry ......................................................... WeBT2 C 

.................................................................................. WeBT2.5 2489

.................................................................................. WeCPT10.5 * 

Pfeifer, Rolf............................................................... ThAT9.8 4168 

Pfeiffer, Kai............................................................... MoAT6.5 241 

.................................................................................. MoBPT10.17 * 

Pflimlin, Jean-Michel................................................. WeBT9.5 2843 

.................................................................................. WeCPT10.26 * 

Pham, Hang.............................................................. ThBT7.4 4496 

.................................................................................. ThCPT10.19 * 

Pham, Minh Tu ......................................................... MoBT7.4 659 

.................................................................................. MoBT9.1 738 

.................................................................................. TuAPT10.16 * 

Phan, Samson.......................................................... WeCT3.4 2992 

.................................................................................. WeCT3.5 2998 

.................................................................................. WeDPT10.7 * 

.................................................................................. WeDPT10.8 * 

Philippsen, Roland.................................................... TuAT7 CC 

.................................................................................. TuAT7.4 1036 

.................................................................................. TuBPT10.16 * 

Phung, Tri Cong ....................................................... TuBT7.7 1447 

Piat, Emmanuel ........................................................ MoAT1.7 39 

Pierce, Matthew........................................................ MoAT9.6 402 

.................................................................................. MoBPT10.27 * 

Pierri, Francesco ...................................................... ThBT1.4 4194 

.................................................................................. ThCPT10.1 * 

Pierrot, François ....................................................... ThDT4 C 

.................................................................................. ThDT4.2 5067 

Pietrusinski, Maciej................................................... ThCT7.1 4893 

Pinciroli, Carlo .......................................................... ThCT9.1 4981 

.................................................................................. ThCT9.8 5027 

Pineda, Elvine Philip................................................. ThAT8.4 4091 

.................................................................................. ThBPT10.22 * 

Pini, Giovanni ........................................................... ThCT9.8 5027 

Pires, Gabriel............................................................ WeBT1.6 2438 

.................................................................................. WeCPT10.3 * 

Pirker, Katrin............................................................. ThAT6.4 3990 

.................................................................................. ThBPT10.16 * 

Pistillo, Antonio......................................................... WeCT4.4 3047 

.................................................................................. WeDT2.4 3413 

.................................................................................. WeDPT10.10 * 

.................................................................................. ThAPT10.4 * 

Pitzer, Benjamin ....................................................... TuBT7.2 1413 

Pivtoraiko, Mihail ...................................................... WeAT5.4 2172 

.................................................................................. WeBPT10.13 * 

Pizarro, Oscar........................................................... TuBT2.3 1242 

.................................................................................. TuBT9.4 1533 

.................................................................................. TuCPT10.22 * 

.................................................................................. ThBT6.6 4455 

.................................................................................. ThCT3.2 4722 

.................................................................................. ThCPT10.18 * 

Plaku, Erion .............................................................. TuBT5.6 1346 

.................................................................................. TuCPT10.12 * 

.................................................................................. WeAT3 CC 

Plumet, Frederic ....................................................... WeDT6.6 3571 

.................................................................................. ThAPT10.15 * 

Poggendorf, Maik ..................................................... WeDT1.3 3355 

Poignet, Philippe....................................................... WeBT3.5 2545 

.................................................................................. WeCPT10.8 * 

Pollefeys, Marc......................................................... TuCT2.1 1618 

.................................................................................. TuCT2.6 1655 

.................................................................................. WeAPT10.6 * 

.................................................................................. ThAT6.7 4012 

Polushin, Ilia G. ........................................................ MoBT7.3 653 

Pomerleau, Francois ................................................ ThAT2.4 3824 

.................................................................................. ThBPT10.4 * 

Popa, Dan................................................................. TuCT3.7 1699 

Popovic, Mila ............................................................ TuAT6.5 987 

.................................................................................. TuBPT10.14 * 

Porez, Mathieu ......................................................... TuCT8.7 1901 

Porquis, Lope Ben.................................................... WeDT3.7 3488 

Porta, Josep M ......................................................... WeBT5.7 2646 


–252– 

Portello, Alban .......................................................... MoAT3.7 137 

Porter, Judson .......................................................... TuAT8.6 1099 

.................................................................................. TuBPT10.21 * 

Pounds, Paul ............................................................ WeBT6.1 2660 

Prankl, Johann.......................................................... TuAT1.5 813 

.................................................................................. TuBPT10.2 * 

Prattichizzo, Domenico............................................. TuAT6.8 1008 

.................................................................................. WeDT7 CC 

Prestes, Edson ......................................................... TuAT9.1 1122 

Preusche, Carsten.................................................... MoAT4.7 177 

.................................................................................. MoBT7.5 665 

.................................................................................. TuAPT10.17 * 

Prorok, Amanda........................................................ WeCT8.3 3241 

.................................................................................. WeCT9.5 3313 

.................................................................................. WeDPT10.26 * 

Protzel, Peter............................................................ TuBT2.2 1234 

Przybylski, Markus.................................................... TuCT6.2 1761 

.................................................................................. TuCT6.5 1781 

.................................................................................. WeAPT10.14 * 

Puerto, Gustavo Armando ........................................ WeAT9.4 2371 

.................................................................................. WeBPT10.25 * 

Puzik, Arnold ............................................................ ThCT2.6 4704 

.................................................................................. ThDPT10.6 * 

Py, Frederic .............................................................. WeCT6.3 

Q 

3132 

Qin, Lei ..................................................................... ThBT7.6 4508 

.................................................................................. ThCPT10.21 * 

Qu, Xingda................................................................ ThCT7.6 4923 

.................................................................................. ThDPT10.21 * 

Quebe, Stephen ....................................................... ThDT8.2 5173 

Quek, Zhan Fan........................................................ WeCT3.4 2992 

.................................................................................. WeCT3.5 2998 

.................................................................................. WeDPT10.7 * 

.................................................................................. WeDPT10.8 * 

Quinn, Roger, D........................................................ MoAT5.4 197 

.................................................................................. MoAT5.6 209 

.................................................................................. MoAT5.7 215 

.................................................................................. MoBPT10.13 * 

.................................................................................. MoBPT10.15 

R 

* 

Radig, Bernd............................................................. ThBT9 C 

.................................................................................. ThBT9.4 4587 

.................................................................................. ThCPT10.25 * 

Radkhah, Katayon .................................................... ThCT5.3 4811 

Radrich, Helmuth...................................................... WeDT1.6 3375 

.................................................................................. ThAPT10.3 * 

Ragan, Matthew ....................................................... WeCT6.5 3147 

.................................................................................. WeDPT10.17 * 

Rai, Vijeth ................................................................. ThCT4.5 4783 

.................................................................................. ThDPT10.11 * 

Raibert, Marc ............................................................ TuPL.1 * 

Rajan, Kanna............................................................ WeCT6.3 3132 

Ramamoorthy, Subramanian.................................... WeDT8.7 3679 

Ramos, Oscar........................................................... ThAT9.2 4127 

Randazzo, Marco ..................................................... ThAT9.7 4161 

Rander, Peter ........................................................... ThAT6.8 4020 

Ranganathan, Ananth............................................... TuCT2.1 1618 

.................................................................................. ThAT6.3 3982 

Rañó, Iñaki ............................................................... WeAT8.8 2346 

Raschendörfer, Lars ................................................. WeDT3.5 3474 

.................................................................................. ThAPT10.8 * 

Rasmussen, Christopher .......................................... ThAT8.3 4084 

Rasolzadeh, Babak .................................................. WeDT1.1 3342 

Ratnasingam, Sivalogeswaran ................................. WeDT3.1 3446 

Rauter, Georg........................................................... WeCT4.7 3068 

Rawlik, Konrad ......................................................... MoBT8.5 718 

.................................................................................. TuAPT10.20 * 

Ray, Christopher....................................................... ThAPT10.16 * 

Raz, Ariel .................................................................. WeDT5.7 3539 

Reckhaus, Michael ................................................... TuAT7.3 1030

Reddy, Prashant....................................................... WeAT8.5 2327 

.................................................................................. WeBPT10.23 * 

Reggente, Matteo..................................................... ThCT9.5 5007 

.................................................................................. ThDPT10.26 * 

Régnier, Stéphane.................................................... TuAT3.3 894 

.................................................................................. TuAT3.6 913 

.................................................................................. TuBPT10.9 * 

.................................................................................. TuCT3.5 1687 

.................................................................................. WeAPT10.8 * 

Rehder, Joern........................................................... MoAT6.3 227 

Reiher, Stephane...................................................... TuCT7.7 1849 

Reilink, Rob .............................................................. TuAT5.3 937 

.................................................................................. WeAT3.1 2076 

Reina, Giulio............................................................. MoAT6.7 255 

Reinecke, Jens......................................................... TuBT6.3 1366 

.................................................................................. ThBT1.7 4215 

Reiter, Austin............................................................ WeAT9.7 2390 

Rekleitis, Georgios ................................................... MoBT6.2 595 

Rekleitis, Ioannis ...................................................... ThDT3 C 

.................................................................................. ThDT3.3 5048 

.................................................................................. ThDT3.4 5054 

Remy, C. David ........................................................ MoAT5.3 190 

.................................................................................. MoBT5.4 562 

.................................................................................. TuAPT10.10 * 

Ren, Hongliang......................................................... WeAT3.2 2083 

Ren, Wei................................................................... ThCT1.5 4645 

.................................................................................. ThDPT10.2 * 

Ren, Xiaofeng........................................................... TuAT1.6 821 

.................................................................................. TuBPT10.3 * 

.................................................................................. ThCT6.3 4850 

Renaud, Pierre ......................................................... WeBT3.8 2564 

Renzaglia, Alessandro.............................................. TuCT2.7 1661 

.................................................................................. WeBT2.1 2460 

Resende, Cassius .................................................... ThAT1.1 3746 

Rétornaz, Philippe .................................................... ThCT4.2 4762 

Revzen, Shai ............................................................ ThCT4.7 4797 

Riazuelo, Luis........................................................... TuBT2.8 1277 

Richa, Rogerio.......................................................... WeCT2.6 2953 

.................................................................................. WeDPT10.6 * 

Richardson, Andrew ................................................. ThAT3.5 3881 

.................................................................................. ThBPT10.8 * 

Richier, Mathieu........................................................ ThBT9.1 4569 

Richtsfeld, Andreas .................................................. TuBT1.5 1201 

.................................................................................. TuCPT10.2 * 

Rickert, Markus......................................................... TuAT7.8 1063 

Riener, Robert .......................................................... WeCT4.7 3068 

Righetti, Ludovic....................................................... MoAT8.2 325 

.................................................................................. MoAT8.8 365 

.................................................................................. ThCT1 CC 

.................................................................................. ThCT1.4 4639 

.................................................................................. ThDPT10.1 * 

Rimon, Elon.............................................................. TuCT6 CC 

Rios-Martinez, Jorge ................................................ WeAT1.8 2014 

Risi, Sebastian.......................................................... WeBT8.7 2802 

Ristic-Durrant, Danijela............................................. TuAT1.1 786 

Ritter, Helge Joachim ............................................... TuBT7.4 1427 

.................................................................................. TuCPT10.16 * 

.................................................................................. WeCT2.5 2947 

.................................................................................. WeDPT10.5 * 

Rituerto, Jorge.......................................................... WeAT9.6 2383 

.................................................................................. WeBPT10.27 * 

Ritz, Robin................................................................ ThDT8.3 5179 

Ritzmann, Roy Earl................................................... MoAT5.7 215 

Rives, Patrick............................................................ WeCT9.6 3320 

.................................................................................. WeDPT10.27 * 

.................................................................................. ThBT2.3 4242 

Riviere, Cameron...................................................... ThBT7.8 4522 

.................................................................................. ThDT7.4 5160 

Roa, Maximo A......................................................... TuCT6.3 1768 

.................................................................................. ThBT5.7 4420 


–253– 

Robert, Thomas........................................................ ThAT5.7 3969 

Robertsson, Anders.................................................. ThCT2.6 4704 

.................................................................................. ThDPT10.6 * 

Robuffo Giordano, Paolo .......................................... MoAT4.5 163 

.................................................................................. MoBPT10.11 * 

.................................................................................. WeAT6.4 2215 

.................................................................................. WeBPT10.16 * 

.................................................................................. WeCT4.3 3039 

Rocco, Paolo ............................................................ WeCT3.1 2971 

Rodemann, Tobias ................................................... MoAT3.2 106 

.................................................................................. MoAT3.6 131 

.................................................................................. MoBPT10.9 * 

.................................................................................. TuBT1.3 1187 

Röder, Thorsten........................................................ WeDT1.6 3375 

.................................................................................. ThAPT10.3 * 

Rodriguez, Alberto.................................................... TuCT6.8 1804 

Roesthuis, Roy ......................................................... WeBT3.7 2557 

Rogers III, John G. ................................................... TuBT2.6 1264 

.................................................................................. TuCPT10.6 * 

Rognini, Giulio .......................................................... ThCT1.8 4664 

Rogoff, Joshua ......................................................... ThCT4.6 4790 

.................................................................................. ThDPT10.12 * 

Rollinson, David........................................................ MoAT5.8 221 

.................................................................................. TuAT8.2 1075 

Romero-Ramirez, Miguel-Angel ............................... WeDT6.6 3571 

.................................................................................. ThAPT10.15 * 

Rosa, Benoît............................................................. TuBT5.5 1339 

.................................................................................. TuCPT10.11 * 

Rotea, Mario ............................................................. TuBT1.2 1180 

Roth, Bernard ........................................................... TuPL C 

Rotinat, Christine ...................................................... MoAT1.8 45 

Rouat, Jean .............................................................. MoBT3.3 518 

Roumeliotis, Stergios................................................ MoAT2.3 65 

.................................................................................. MoAT2.8 98 

.................................................................................. MoBT2.6 502 

.................................................................................. TuAPT10.6 * 

Rucker, Caleb........................................................... WeBT3.1 2517 

.................................................................................. ThAT1.4 3764 

.................................................................................. ThBPT10.1 * 

Ruehr, Thomas......................................................... ThBT2.6 4263 

.................................................................................. ThCPT10.6 * 

Ruepp, Oliver............................................................ WeCT9.3 3297 

Ruggiero, Fabio ........................................................ ThBT1.4 4194 

.................................................................................. ThCPT10.1 * 

Ruhnke, Michael....................................................... TuBT2.4 1249 

.................................................................................. TuCPT10.4 * 

Ruini, Fabio .............................................................. ThCT9.6 5015 

.................................................................................. ThDPT10.27 * 

Ruppel, Alexander .................................................... WeAT9.3 2365 

Rus, Daniela ............................................................. MoBT9.4 756 

.................................................................................. TuAPT10.22 * 

.................................................................................. WeCT5.2 3087 

.................................................................................. WeDT8.2 3645 

.................................................................................. ThCT4.8 4803 

.................................................................................. ThDT8.4 5187 

Russell, Paul T. ........................................................ WeBT3.1 2517 

Russell, Shawn......................................................... ThAT5.7 3969 

Rusu, Radu Bogdan ................................................. ThBT2.4 4249 

.................................................................................. ThCPT10.4 * 

Rüther, Matthias ....................................................... ThAT6.4 3990 

.................................................................................. ThBPT10.16 * 

Rutter, Brandon ........................................................ MoAT5.7 215 

Rybok, Lukas............................................................ ThCT5.4 4819 

.................................................................................. ThDPT10.13 * 

Rydén, Fredrik .......................................................... WeBT4.8 2614 

Ryden, Thomas ........................................................ MoAT4.1 * 

Rye, David ................................................................ MoBT8.6 725 

.................................................................................. TuAPT10.21 * 

Ryu, Jee-Hwan ......................................................... MoAT4.7 177 

Ryu, Seok Chang ..................................................... WeBT3.8 2564

S 

Saab, Layale............................................................. ThAT9.2 4127 

Saad, Maarouf.......................................................... WeAT8.7 2340 

Saarinen, Jari Pekka ................................................ MoAT2.2 59 

Sabattini, Lorenzo..................................................... WeAT8.4 2321 

.................................................................................. WeBPT10.22 * 

Sadeghian, Hamid.................................................... ThAT1.2 3752 

Saeedi Gharahbolagh, Sajad ................................... TuAT2.3 853 

.................................................................................. TuAT2.7 880 

Saenko, Kate............................................................ TuAT1.2 793 

Saffiotti, Alessandro.................................................. ThCT9.5 5007 

.................................................................................. ThDPT10.26 * 

Sahai, Ranjana......................................................... TuCT9.2 1919 

Saida, Masao............................................................ MoAT7.8 311 

Saito, Takefumi......................................................... ThCT7.2 4899 

Sakagami, Norimitsu ................................................ ThCT3.7 4756 

Sakamoto, Kiyoshi.................................................... WeAT2.3 2036 

Sakurai, Tatsuma ..................................................... WeDT3.8 3494 

Salaris, Paolo ........................................................... WeBT9.1 2817 

.................................................................................. ThAT1.5 3770 

.................................................................................. ThBPT10.2 * 

Salisbury, Kenneth ................................................... WeBT4.1 2570 

Samarasekera, Supun.............................................. ThAT3.4 3874 

.................................................................................. ThBPT10.7 * 

Sammut, Claude....................................................... MoBT8.7 732 

Sanahuja, Guillaume ................................................ WeBT6.4 2682 

.................................................................................. WeCPT10.16 * 

Sanchez, Fernando .................................................. TuBT8.8 1507 

Sanchez Fibla, Marti................................................. TuAT8.8 1115 

Sanchez Plazas, Oscar ............................................ WeCT5.4 3101 

.................................................................................. WeDPT10.13 * 

Sandini, Giulio .......................................................... ThAT9.7 4161 

Sandini, Giulio .......................................................... ThBT4.3 4354 

Sanemasa, Toru....................................................... TuCT5.2 1717 

Sangkyu, Yi .............................................................. ThAT6.5 3998 

.................................................................................. ThBPT10.17 * 

Sani, Hamidreza....................................................... TuCT9.3 1927 

Sankai, Yoshiyuki ..................................................... TuCT5.5 1737 

.................................................................................. WeAPT10.11 * 

.................................................................................. ThCT7.2 4899 

Sano, Kumiko ........................................................... WeDT3.3 3460 

Santos, Cristina ........................................................ WeAT7.7 2286 

Santos, Milton César Paes....................................... WeDT7.6 3620 

.................................................................................. ThAPT10.18 * 

Santos Sanchez, Omar Jacobo................................ WeBT6.4 2682 

.................................................................................. WeCPT10.16 * 

Santos-Victor, José .................................................. ThCT5.5 4826 

.................................................................................. ThDPT10.14 * 

Sarcinelli-Filho, Mario ............................................... ThAT1.1 3746 

Sardellitti, Irene......................................................... MoAT9.2 378 

.................................................................................. WeCT3 CC 

.................................................................................. ThAT7 C 

.................................................................................. ThAT7.1 4026 

Sarholz, Frederik ...................................................... ThBT9.4 4587 

.................................................................................. ThCPT10.25 * 

Saripalli, Srikanth...................................................... WeAT6 O 

.................................................................................. WeBT6 C 

.................................................................................. WeBT6 O 

Sarria, Javier Francisco............................................ TuBT8.8 1507 

Saruwatari, Hiroshi ................................................... MoBT3.2 * 

Sasaki, Hironobu ...................................................... ThCT2.1 4670 

Sato, Fuyuki.............................................................. WeCT7.2 3179 

Sato, Katsunari......................................................... MoAT4 O 

.................................................................................. MoAT4.4 157 

.................................................................................. MoBPT10.10 * 

Sato, Takahide ......................................................... TuCT8.3 1875 

.................................................................................. TuCT8.4 1881 

.................................................................................. WeAPT10.19 * 

Sato, Tomomasa ...................................................... WeDT2.5 3419 

.................................................................................. WeDT7.3 3601 


–254– 

.................................................................................. ThAPT10.5 * 

Scaccia, Milena ........................................................ ThDT3.4 5054 

Scandaroli, Glauco Garcia........................................ WeCT9.8 3335 

Scaramuzza, Davide ................................................ TuCT2 CC 

.................................................................................. TuCT2.7 1661 

.................................................................................. ThBT6.8 4469 

Schaal, Stefan .......................................................... MoAT8.2 325 

.................................................................................. MoAT8.8 365 

.................................................................................. WeDT2.3 3407 

.................................................................................. ThCT1.4 4639 

.................................................................................. ThDPT10.1 * 

Schäfer, Felix............................................................ TuAT5.4 943 

.................................................................................. TuBPT10.10 * 

Schairer, Timo .......................................................... ThBT2.1 4229 

Schauerte, Boris ....................................................... TuBT1.1 1173 

Scherer, Sebastian ................................................... MoAT6.3 227 

.................................................................................. WeAT6.3 2207 

Scheuer, Alexis......................................................... ThAT8.6 4103 

.................................................................................. ThBPT10.24 * 

Schiele, Andre .......................................................... WeAT4.8 2158 

Schilling, Andreas..................................................... ThBT2.1 4229 

Schindler, Thorsten .................................................. TuBT7.5 1433 

.................................................................................. TuCPT10.17 * 

Schlaefer, Steven ..................................................... MoAT9.7 408 

Schlegel, Christian.................................................... WeAT2 CC 

.................................................................................. WeAT2.7 2064 

Schmidt-Rohr, Sven R.............................................. ThCT1.3 4633 

Schmidt-Wetekam, Christopher................................ WeBT7.5 2741 

.................................................................................. WeCPT10.20 * 

.................................................................................. ThAT8.5 4097 

.................................................................................. ThBPT10.23 * 

Schmidts, Alexander Markus.................................... TuAT6.7 1002 

Schmit, Nicolas......................................................... MoBT9.5 762 

.................................................................................. TuAPT10.23 * 

Schneider, Axel ........................................................ ThAT1.6 3776 

.................................................................................. ThBPT10.3 * 

Schneider, Jeff.......................................................... MoAT8.7 357 

.................................................................................. TuAT8.1 1069 

Schneider, Robert..................................................... TuBT5.3 1327 

Schneider, Ulrich ...................................................... ThCT2.6 4704 

.................................................................................. ThDPT10.6 * 

Schoelkopf, Bernhard ............................................... MoAT8.3 332 

.................................................................................. MoBT8.2 698 

Schoen, Timothy Ryan ............................................. ThCT4.8 4803 

Schrimpf, Johannes.................................................. WeBT9.8 2861 

Schroeter, Christof.................................................... WeBT1.5 2430 

Schultz, Tanja........................................................... ThCT5.4 4819 

.................................................................................. ThDPT10.13 * 

Schultz, Ulrik Pagh ................................................... WeDT8.4 3659 

.................................................................................. ThAPT10.19 * 

Schulz, Dirk .............................................................. WeDT7.5 3614 

.................................................................................. ThAPT10.17 * 

Schwager, Mac......................................................... ThDT8 CC 

.................................................................................. ThDT8.4 5187 

Schwartz, Ira............................................................. ThAT4.1 * 

.................................................................................. ThAT4.3 3905 

Schwartz, Matthijs P ................................................. TuAT5.3 937 

Schwartz, Maxim ...................................................... TuAT9.6 1154 

.................................................................................. TuBPT10.24 * 

Scioni, Enea ............................................................. WeCT4.2 3031 

Secchi, Cristian......................................................... MoAT4 O 

.................................................................................. MoAT4.5 163 

.................................................................................. MoBPT10.11 * 

.................................................................................. WeAT8.4 2321 

.................................................................................. WeBPT10.22 * 

.................................................................................. ThBT9.8 4615 

Sedef, Mert ............................................................... WeBT3.4 2539 

.................................................................................. WeCPT10.7 * 

Seegmiller, Neal Andrew .......................................... MoBT6.4 607 

.................................................................................. MoBT6.5 613

.................................................................................. TuAPT10.13 * 

.................................................................................. TuAPT10.14 * 

Seipel, Justin ............................................................ MoAT5 C 

.................................................................................. MoAT5 O 

.................................................................................. MoAT5.5 203 

.................................................................................. MoBT5 O 

.................................................................................. MoBPT10.14 * 

Seki, Hiroya .............................................................. WeAT8.3 2314 

Sekiyama, Kosuke.................................................... ThCT2.1 4670 

Senda, Kei................................................................ WeAT7.5 2274 

.................................................................................. WeAT7.6 2280 

.................................................................................. WeBPT10.20 * 

.................................................................................. WeBPT10.21 * 

Sentis, Luis............................................................... TuAT7.4 1036 

.................................................................................. TuBPT10.16 * 

.................................................................................. WeAT7 C 

.................................................................................. WeAT7.4 2267 

.................................................................................. WeBPT10.19 * 

Sepp, Wolfgang........................................................ WeDT7.7 3626 

Seps, Monika............................................................ WeDT3.2 3454 

Seyfarth, Andre......................................................... TuCT7.1 1811 

Shah, Preyas............................................................ WeCT3.4 2992 

.................................................................................. WeDPT10.7 * 

Shah, Shridhar.......................................................... ThAT4.4 3911 

.................................................................................. ThBPT10.10 * 

Shahbazi, Hossein.................................................... TuBT2.1 1228 

Shakhimardanov, Azamat ........................................ TuAT7.3 1030 

Shamma, Jeff ........................................................... ThCT4.3 4770 

Shammas, Elie ......................................................... ThBT3 CC 

.................................................................................. ThBT3.7 4329 

Shams, Sarmad........................................................ ThDT6.3 5127 

Shang, Jianzhong..................................................... TuAT5.5 949 

.................................................................................. TuBPT10.11 * 

Shao, Xiaowei........................................................... WeAT2.3 2036 

Shaw, Kendrick......................................................... MoAT5.4 197 

.................................................................................. MoBPT10.13 * 

Sheh, Raymond Ka-Man .......................................... MoBT8.7 732 

Shell, Dylan .............................................................. ThCT9 C 

.................................................................................. ThCT9.4 5001 

.................................................................................. ThDPT10.25 * 

Shen, Jinglin............................................................. TuBT1.2 1180 

Shen, Lincheng......................................................... MoBT5.5 568 

.................................................................................. TuAPT10.11 * 

Shen, Yantao............................................................ WeAT4.4 2133 

.................................................................................. WeBPT10.10 * 

.................................................................................. ThBT9.2 4575 

Shende, Apoorva...................................................... WeCT8.1 3227 

Sheng, Weihua......................................................... WeCT1 C 

.................................................................................. WeCT1.2 2873 

.................................................................................. WeDT2.1 3395 

Sherbert, Robert....................................................... TuAT7.2 1022 

Sherikov, Aleksander................................................ WeAT7.8 2292 

Shi, Chaoyang.......................................................... WeAT3.7 2115 

Shi, Yun.................................................................... WeAT2.3 2036 

Shibasaki, Ryosuke.................................................. WeAT2.3 2036 

Shibata, Mizuho........................................................ ThCT3.7 4756 

Shida, Kazuya .......................................................... TuBT8.7 1499 

Shih, Albert J. ........................................................... TuAT5.7 961 

Shiller, Zvi................................................................. WeDT5.7 3539 

Shimojo, Makoto....................................................... TuCT9.7 1954 

.................................................................................. ThAT7.4 4048 

.................................................................................. ThCT3.4 4735 

.................................................................................. ThDPT10.7 * 

Shimosaka, Masamichi............................................. WeDT2.5 3419 

.................................................................................. WeDT7.3 3601 

.................................................................................. ThAPT10.5 * 

Shin, Dongik ............................................................. ThDT6.3 5127 

Shin, Dongjun........................................................... TuCT7.4 1830 

.................................................................................. WeAPT10.16 * 

.................................................................................. WeCT3.4 2992 


–255– 

.................................................................................. WeCT3.5 2998 

.................................................................................. WeDPT10.7 * 

.................................................................................. WeDPT10.8 * 

Shin, Kyoosik............................................................ ThDT6.3 5127 

Shin, Seung Hoon .................................................... TuBT7.7 1447 

Shintake, Jun............................................................ ThCT3.4 4735 

.................................................................................. ThDPT10.7 * 

Shiriaev, Anton ......................................................... ThDT5.2 5094 

Shkolnik, Alexander.................................................. ThBT3.4 4307 

.................................................................................. ThCPT10.7 * 

Shkurti, Florian ......................................................... ThDT3.3 5048 

.................................................................................. ThDT3.4 5054 

Shomin, Michael ....................................................... WeBT6.2 2668 

Shou, Kaiyu .............................................................. TuBT3.5 1297 

.................................................................................. TuCPT10.8 * 

Shyr, Alex ................................................................. TuAT1.2 793 

Siciliano, Bruno......................................................... TuCT6.4 1775 

.................................................................................. WeAPT10.13 * 

.................................................................................. ThAT1.2 3752 

Siegwart, Roland ...................................................... MoAT2.5 79 

.................................................................................. MoAT5.3 190 

.................................................................................. MoBT5.4 562 

.................................................................................. MoBPT10.5 * 

.................................................................................. TuAPT10.10 * 

.................................................................................. TuCT2.7 1661 

.................................................................................. WeAT6.5 2223 

.................................................................................. WeAT6.7 2235 

.................................................................................. WeAT6.8 2242 

.................................................................................. WeBT6.6 2694 

.................................................................................. WeBPT10.17 * 

.................................................................................. WeCPT10.18 * 

.................................................................................. ThAT2.4 3824 

.................................................................................. ThBPT10.4 * 

Sigrist, Roland .......................................................... WeCT4.7 3068 

Silveira, Geraldo ....................................................... WeCT9.8 3335 

Silver, David ............................................................. MoBT2.7 510 

Simaan, Nabil ........................................................... WeAT9.7 2390 

Simonin, Olivier ........................................................ ThAT8.6 4103 

.................................................................................. ThBPT10.24 * 

Simpson, Jason ........................................................ ThCT8.8 4975 

Singh, Sanjiv............................................................. MoAT6.3 227 

.................................................................................. WeAT6.3 2207 

.................................................................................. WeAT9.1 2352 

Singh, Surya ............................................................. MoBT8.6 725 

.................................................................................. TuAPT10.21 * 

Singleton, John......................................................... TuCT3.5 1687 

.................................................................................. WeAPT10.8 * 

Sinnet, Ryan W......................................................... TuCT5.3 1723 

Sirouspour, Shahin ................................................... MoBT7.2 645 

Sisbot, Emrah Akin ................................................... WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Sitti, Metin................................................................. MoBT5.3 556 

.................................................................................. TuBT3.4 1291 

.................................................................................. TuCT3.1 * 

.................................................................................. TuCT3.3 1674 

.................................................................................. TuCT3.5 1687 

.................................................................................. TuCPT10.7 * 

.................................................................................. WeAPT10.8 * 

.................................................................................. ThAT4.8 3937 

Sivalingam, Ravishankar .......................................... TuAT9.8 1167 

Sjöö, Kristoffer .......................................................... TuBT9.1 1513 

Skachek, Sergey ...................................................... WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Skantze, Gabriel ....................................................... WeDT1.1 3342 

Skocaj, Danijel.......................................................... WeDT1.8 3387 

Skourup, Charlotte.................................................... MoBPT10.16 * 

Smart, William .......................................................... ThAT8 CC 

.................................................................................. ThAT8.8 4115 

Smeraldi, Fabrizio..................................................... TuBT9.3 1526 

Smith, Ryan .............................................................. WeCT6 C

.................................................................................. WeCT6 O 

.................................................................................. WeDT6 O 

Smith, Stephen L...................................................... WeCT5.2 3087 

.................................................................................. WeDT8 CC 

.................................................................................. WeDT8.2 3645 

Smith, William........................................................... WeCT3.6 3004 

.................................................................................. WeDPT10.9 * 

Smits, Ruben............................................................ WeCT4.2 3031 

.................................................................................. ThCT2.3 4684 

Soccol, Dean ............................................................ ThCT8.2 4935 

Solanes, Ernesto ...................................................... ThBT3.8 4335 

Soltero, Daniel E....................................................... WeDT8.2 3645 

Son, Hyoung Il.......................................................... WeCT4.3 3039 

Sone, Satoshi ........................................................... TuCT9.7 1954 

Song, Dan................................................................. TuAT6.4 980 

.................................................................................. TuBPT10.13 * 

Song, Yixu ................................................................ WeAT9.2 2359 

Sornmo, Olof ............................................................ ThCT2.6 4704 

.................................................................................. ThDPT10.6 * 

Sotzek, Alice............................................................. WeDT1.6 3375 

.................................................................................. ThAPT10.3 * 

Soueres, Philippe ..................................................... ThAT9.2 4127 

Soulignac, Michaël ................................................... WeDT5.4 3519 

.................................................................................. ThAPT10.10 * 

Sousa, João.............................................................. WeCT6.6 3154 

.................................................................................. WeDPT10.18 * 

Sousa Junior, Samuel Felix de................................. WeAT1.2 1974 

Spalanzani, Anne ..................................................... WeAT1.8 2014 

Spampinato, Giacomo.............................................. TuCT2.2 1626 

Spenko, Matthew...................................................... MoAT7 CC 

.................................................................................. MoAT7.7 305 

Spinello, Luciano ...................................................... ThAT2.6 3838 

.................................................................................. ThAT2.7 3844 

.................................................................................. ThBPT10.6 * 

Srinivasa, Siddhartha ............................................... TuCT6.8 1804 

.................................................................................. WeAT1.4 1986 

.................................................................................. WeBPT10.1 * 

Srinivasan, Mandyam............................................... WePL.1 * 

.................................................................................. ThCT8.2 4935 

Stachniss, Cyrill........................................................ MoAT2.6 86 

.................................................................................. MoBPT10.6 * 

.................................................................................. TuAT2.5 865 

.................................................................................. TuBPT10.5 * 

.................................................................................. WeAT5.5 2180 

.................................................................................. WeBPT10.14 * 

.................................................................................. ThBT2 C 

.................................................................................. ThBT2.4 4249 

.................................................................................. ThCPT10.4 * 

Stanley, Kenneth ...................................................... WeBT8.7 2802 

Stasse, Olivier .......................................................... ThBT5.5 4408 

.................................................................................. ThCPT10.14 * 

Steck, Andreas ......................................................... WeAT2.7 2064 

Steder, Bastian......................................................... TuBT2.4 1249 

.................................................................................. TuCPT10.4 * 

Steffen, Jan .............................................................. WeCT2.5 2947 

.................................................................................. WeDPT10.5 * 

Stegagno, Paolo....................................................... MoBT2.1 469 

Stein, David .............................................................. ThCT4.8 4803 

Steinberg, Daniel...................................................... TuBT9.4 1533 

.................................................................................. TuCPT10.22 * 

.................................................................................. ThCT3.2 4722 

Steinwender, Jasmin ................................................ WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Stelzer, Annett.......................................................... WeCPT10.6 * 

Stentz, Anthony ........................................................ MoBT2.7 510 

.................................................................................. WeBT8.4 2784 

.................................................................................. WeCPT10.22 * 

Sterkers, Olivier........................................................ WeBT3.3 2532 

Stiefelhagen, Rainer................................................. TuBT1.1 1173 

.................................................................................. ThCT5.4 4819 


–256– 

.................................................................................. ThDPT10.13 * 

Stilman, Mike ............................................................ WeBT5.6 2640 

.................................................................................. WeCPT10.15 * 

.................................................................................. ThCT1 C 

.................................................................................. ThCT1.2 4627 

Stilwell, Daniel .......................................................... TuAT9.4 1140 

.................................................................................. TuBPT10.22 * 

.................................................................................. WeCT8.1 3227 

Stirling, Leia.............................................................. ThBT7.3 4488 

Stirling, Timothy........................................................ ThCT9.8 5027 

Stoianovici, Dan........................................................ TuAT5.4 943 

.................................................................................. TuBPT10.10 * 

Stolle, Christian ........................................................ TuAT3.3 894 

Stone, Peter.............................................................. ThBT9.3 4581 

Stoy, Kasper ............................................................. WeDT8 C 

.................................................................................. WeDT8.4 3659 

.................................................................................. ThAPT10.19 * 

Stramigioli, Stefano .................................................. TuAT5.3 937 

.................................................................................. WeAT3 C 

.................................................................................. WeAT3.1 2076 

Strasser, Wolfgang ................................................... ThBT2.1 4229 

Strausser, Katherine................................................. ThCT7.4 4911 

.................................................................................. ThDPT10.19 * 

Strefling, Paul ........................................................... ThCT3.6 4749 

.................................................................................. ThDPT10.9 * 

Strom, Johannes H................................................... ThBT2.7 4271 

Studley, Matthew ...................................................... ThCT9.3 4995 

Stulp, Freek .............................................................. MoAT8.2 325 

.................................................................................. WeDT2.3 3407 

Stump, Ethan............................................................ MoBT2 CC 

.................................................................................. WeBT6.8 2708 

Stumpf, Jan Carsten................................................. ThDT8.1 5166 

Sturm, Peter ............................................................. ThAT6.6 4006 

.................................................................................. ThBPT10.18 * 

Su, Hu....................................................................... WeDT2.7 3434 

Sucan, Ioan Alexandru ............................................. ThCT1.1 4621 

Suetani, Hiromichi .................................................... WeBT7.1 2715 

Sugahara, Yusuke .................................................... ThBT9.5 4593 

.................................................................................. ThCPT10.26 * 

Sugano, Shigeki ....................................................... WeDT9.7 3734 

.................................................................................. ThAT1.8 3792 

.................................................................................. ThBT5 O 

.................................................................................. ThCT5 CC 

.................................................................................. ThCT5 O 

Sugi, Masao.............................................................. ThCT2.5 4698 

Sugimoto, Norikazu .................................................. WeCT7.3 3185 

Sugimoto, Shigeki..................................................... ThBT6.7 4463 

Sugimoto, Yasuhiro .................................................. TuCT7.2 1817 

Sugiura, Komei ......................................................... MoAT8.6 350 

.................................................................................. MoBPT10.24 * 

Suh, Il Hong.............................................................. WeDT7.2 3595 

Sujit, P.B................................................................... WeCT6.6 3154 

.................................................................................. WeDPT10.18 * 

Sukhatme, Gaurav.................................................... WeCT6.3 3132 

.................................................................................. WeCT6.5 3147 

.................................................................................. WeCT6.7 3160 

.................................................................................. WeDT6.5 3564 

.................................................................................. WeDPT10.17 * 

.................................................................................. ThAPT10.14 * 

.................................................................................. ThBT4.2 4348 

Sukkarieh, Salah ...................................................... ThBT2.5 4256 

.................................................................................. ThCPT10.5 * 

Sukthankar, Gita....................................................... WeBT4.4 2590 

.................................................................................. WeCPT10.10 * 

Sullivan, Brian........................................................... TuBT1.4 1194 

.................................................................................. TuCPT10.1 * 

Sullivan, Jenny ......................................................... TuCT5.1 1711 

Sumioka, Hidenobu .................................................. ThAT9.8 4168 

Sun, Baiqing ............................................................. ThCT2.1 4670 

Sun, Dali ................................................................... WeCT8.8 3276

Sun, Dong................................................................. MoBT1.6 451 

.................................................................................. TuAPT10.3 * 

Sun, Xiaochuan ........................................................ WeAT3.4 2096 

.................................................................................. WeBPT10.7 * 

Sun, Yi...................................................................... ThDT3.1 5035 

Sun, Yu..................................................................... ThAT6.1 * 

Sünderhauf, Niko...................................................... TuBT2.2 1234 

Suppa, Michael......................................................... TuCT9.4 1933 

.................................................................................. WeAPT10.22 * 

.................................................................................. WeCT9.3 3297 

Suzuki, Kouki............................................................ ThBT9.5 4593 

.................................................................................. ThCPT10.26 * 

Suzuki, Shota ........................................................... TuCT8.6 1895 

.................................................................................. WeAPT10.21 * 

Suzuki, Takahiro....................................................... TuBT1.7 1214 

Suzuki, Yosuke......................................................... TuCT9.7 1954 

Svec, Petr................................................................. TuAT9.6 1154 

.................................................................................. TuBPT10.24 * 

Svinin, Mikhail........................................................... ThBT4.8 4386 

Swaney, Philip.......................................................... WeBT3.1 2517 

Szczuka, Roman ...................................................... WeDT8.3 3653 

Szewczyk, Jérôme.................................................... TuBT5.4 1333 

.................................................................................. TuBT5.5 1339 

.................................................................................. TuCPT10.10 * 

.................................................................................. TuCPT10.11 * 

Sznitman, Raphael ................................................... WeCT2.6 2953 

.................................................................................. WeDPT10.6 * 

Szwaykowska, Klementyna...................................... WeDT6.8 

T 

3583 

Tabak, Ahmet Fatih .................................................. MoBT1.8 463 

Tachi, Susumu.......................................................... MoAT4.4 157 

.................................................................................. MoBPT10.10 * 

Tadakuma, Kenjiro ................................................... TuBT6.6 1386 

.................................................................................. TuBT6.7 1392 

.................................................................................. TuCPT10.15 * 

.................................................................................. ThAT7.4 4048 

.................................................................................. ThBPT10.19 * 

Tadakuma, Riichiro................................................... ThAT7.4 4048 

.................................................................................. ThBPT10.19 * 

Tadano, Kotaro......................................................... TuAT5.2 931 

Tadokoro, Satoshi .................................................... MoAT6 C 

.................................................................................. MoAT6 O 

.................................................................................. MoBT6 CC 

.................................................................................. MoBT6 O 

.................................................................................. WeDT3.7 3488 

.................................................................................. WeDT3.8 3494 

Tae, Kyung ............................................................... TuAT5.8 967 

Tagawa, Yasutaka.................................................... ThCT2.7 4710 

Taghirad, Hamid....................................................... TuAT9.2 1128 

Taguchi, Yuichi......................................................... ThCT2.4 4690 

.................................................................................. ThDPT10.4 * 

Tahara, Kenji ............................................................ TuCT7.6 1843 

.................................................................................. WeAPT10.18 * 

.................................................................................. ThBT1.5 4201 

.................................................................................. ThCPT10.2 * 

Taji, Kouichi.............................................................. WeBT7.4 2735 

.................................................................................. WeCPT10.19 * 

Takahashi, Jun ......................................................... WeCT7.2 3179 

Takahashi, Koji......................................................... ThBT6.4 4442 

.................................................................................. ThCPT10.16 * 

Takaki, Takeshi ........................................................ TuBT1.6 1208 

.................................................................................. TuCPT10.3 * 

Takanishi, Atsuo....................................................... WeCT7.8 3221 

Takano, Wataru........................................................ WeDT2.2 3401 

Takaoka, Shunichi.................................................... TuAT8.3 1081 

Takayama, Leila ....................................................... WeBT1.8 2452 

Takemura, Noriko..................................................... WeCT1.1 2867 

Takeshita, Keisuke ................................................... MoAT4.4 157 

.................................................................................. MoBPT10.10 * 

Takeuchi, Masaru..................................................... MoBT1.7 457 


–257– 

Takhmar, Amir .......................................................... MoBT7.3 653 

Takiguchi, Kingo ....................................................... MoAT1.1 1 

Takubo, Tomohito..................................................... MoAT1.5 25 

.................................................................................. MoBT1.2 427 

.................................................................................. MoBPT10.2 * 

.................................................................................. TuAT1.4 807 

.................................................................................. TuBPT10.1 * 

Takubo, Toshio......................................................... WeBT1.4 2423 

.................................................................................. WeCPT10.1 * 

Takumi, Yoshida....................................................... MoAT4.4 157 

.................................................................................. MoBPT10.10 * 

Tamjidi, Amir Hossein............................................... TuAT9.2 1128 

Tamura, Yusuke ....................................................... WeCT7.6 3207 

.................................................................................. WeDPT10.21 * 

Tan, Jindong............................................................. TuCT2.8 1668 

Tan, Min.................................................................... WeDT2.7 3434 

Tan, Sia Nguan Eugene ........................................... ThCT7.3 4905 

Tan, Xiaobo .............................................................. MoBT5.8 588 

Tanaka, Daiki............................................................ WeBT7.6 2747 

.................................................................................. WeCPT10.21 * 

Tanaka, Fumihide..................................................... WeBT1.2 2409 

Tanaka, Kanji............................................................ TuAT2.6 872 

.................................................................................. TuBPT10.6 * 

Tanaka, Yoshiyuki .................................................... TuCT5.2 1717 

Tang, Chaoquan....................................................... TuCT8.2 1869 

Tangorra, James ...................................................... MoBT5.7 580 

Tani, Atsushi............................................................. WeBT1.4 2423 

.................................................................................. WeCPT10.1 * 

Tanikawa, Tamio ...................................................... MoAT1.5 25 

.................................................................................. MoBPT10.2 * 

Tanner, Herbert G. ................................................... ThAT4.4 3911 

.................................................................................. ThBPT10.10 * 

Tao, Xinyong ............................................................ TuCT3.8 1705 

Tardos, Juan D. ........................................................ MoAT2 C 

.................................................................................. MoAT2.1 51 

.................................................................................. TuBT2.8 1277 

Täubig, Holger .......................................................... TuCT1.4 1585 

.................................................................................. WeAPT10.1 * 

Tavakoli, Mahdi ........................................................ MoBT7.4 659 

.................................................................................. MoBT9.1 738 

.................................................................................. TuAPT10.16 * 

Tavakoli, Mahmoud .................................................. ThBT2.8 4277 

Tayama, Munenori.................................................... ThCT7.2 4899 

Taylor, Brian ............................................................. MoAT5.7 215 

Taylor, Camillo Jose ................................................. TuAT7.6 1048 

.................................................................................. TuBPT10.18 * 

.................................................................................. ThCT4.4 4776 

.................................................................................. ThDPT10.10 * 

Taylor, Russell H. ..................................................... MoBT7.1 639 

.................................................................................. WeCT2.6 2953 

.................................................................................. WeDPT10.6 * 

Tedrake, Russ .......................................................... WeBT6.7 2700 

Tegopoulou, Anastasia............................................. WeCT2.1 2922 

Tehrani Nik Nejad, Hossein...................................... ThBT6.4 4442 

.................................................................................. ThCPT10.16 * 

Teller, Seth ............................................................... ThBT3.4 4307 

.................................................................................. ThCPT10.7 * 

Temel, Fatma Zeynep .............................................. MoBT1.8 463 

Tenenholtz, Neil........................................................ TuBT5.3 1327 

Tenorth, Moritz ......................................................... ThBT2.6 4263 

.................................................................................. ThCPT10.6 * 

ter Mors, Adriaan W.................................................. WeAT5.3 2166 

Terada, Kazuki ......................................................... TuCT9.7 1954 

.................................................................................. ThAT7.4 4048 

.................................................................................. ThBPT10.19 * 

Tercero Villagran, Carlos Rafael .............................. MoBT1.4 439 

.................................................................................. TuAPT10.1 * 

.................................................................................. WeAT3.7 2115 

Terekhov, Alexander V. ............................................ ThBT4.4 4360 

.................................................................................. ThCPT10.10 *

Tesch, Matthew ........................................................ MoAT8.7 357 

.................................................................................. TuAT8.1 1069 

Teuliere, Celine ........................................................ ThCT8.1 4929 

Tews, Ashley Desmond............................................ TuAT1 CC 

.................................................................................. TuAT1.8 834 

Thakur, Atul .............................................................. TuAT9.6 1154 

.................................................................................. TuBPT10.24 * 

Theodorou, Evangelos ............................................. MoAT8.2 325 

.................................................................................. WeDT2.3 3407 

Thielmann, Sophie Charlotte Franziska ................... WeCT4.1 3023 

Thobbi, Anand .......................................................... WeCT1.2 2873 

Thompson, David ..................................................... WeCT6.4 3140 

.................................................................................. WeDPT10.16 * 

Thompson, Paul ....................................................... ThBT2.2 4236 

Thorson, Ivar ............................................................ MoAT9.4 390 

.................................................................................. MoBPT10.25 * 

Thrun, Sebastian ...................................................... ThPT11.1 * 

Thuilot, Benoit........................................................... ThAT8.1 4072 

.................................................................................. ThBT9.1 4569 

Thurrowgood, Saul ................................................... ThCT8.2 4935 

Tian, Weicheng......................................................... TuCT8.1 1863 

Tipaldi, Gian Diego................................................... WeAT1.1 1968 

Tobergte, Andreas.................................................... WeCT4.1 3023 

Tokekar, Pratap........................................................ MoBT2.4 488 

.................................................................................. TuAPT10.4 * 

Tokuda, Isao............................................................. WeBT7.6 2747 

.................................................................................. WeCPT10.21 * 

Tolley, Michael Thomas............................................ ThBT4.5 4366 

.................................................................................. ThCPT10.11 * 

Tombari, Federico .................................................... ThCT6.4 4857 

.................................................................................. ThDPT10.16 * 

Tominaga, Shoji........................................................ WeDT2.5 3419 

.................................................................................. ThAPT10.5 * 

Tomita, Kyohei.......................................................... MoAT1.3 13 

Tomiyama, Ken ........................................................ WeBT2.2 2466 

Tomizuka, Masayoshi............................................... TuCT8.5 1887 

.................................................................................. WeAPT10.20 * 

.................................................................................. WeBT2.3 2474 

Tomlin, Claire ........................................................... WeCT3.2 2979 

Tong, Chi Hay........................................................... MoBT6.8 631 

Torfs, Serge.............................................................. ThBT7.1 4477 

Tornero, Josep ......................................................... ThBT3.8 4335 

Torres, Luis G........................................................... ThDT7.3 5153 

Tow, Adela................................................................ ThCT7.6 4923 

.................................................................................. ThDPT10.21 * 

Toyama, Shigeki....................................................... MoBT9.3 750 

Transeth, Aksel Andreas .......................................... MoAT6.6 247 

.................................................................................. MoBPT10.18 * 

Tremblay, Charles .................................................... TuBT3.3 1285 

Trentini, Michael ....................................................... TuAT2.3 853 

.................................................................................. TuAT2.7 880 

Trevor, Alexander J B............................................... TuBT2.6 1264 

.................................................................................. TuCPT10.6 * 

Trianni, Vito .............................................................. ThCT9.8 5027 

Trinkle, Jeff............................................................... TuBT7 CC 

.................................................................................. TuBT7.5 1433 

.................................................................................. TuCT6 C 

.................................................................................. TuCPT10.17 * 

Troiani, Chiara.......................................................... WeBT2.1 2460 

Troni, Giancarlo ........................................................ WeDT9.5 3722 

.................................................................................. ThAPT10.23 * 

Tsagarakis, Nikolaos ................................................ MoAT8.1 318 

.................................................................................. MoAT9.2 378 

.................................................................................. ThAT7.1 4026 

Tsakiris, Dimitris ....................................................... ThAT7.5 4054 

.................................................................................. ThBPT10.20 * 

Tsiotras, Panagiotis.................................................. WeDT5.1 3501 

.................................................................................. WeDT5.2 3507 

.................................................................................. WeDT5.6 3533 

.................................................................................. ThAPT10.12 * 


–258– 

Tsuchiya, Kazuo ....................................................... WeAT7.5 2274 

.................................................................................. WeAT7.6 2280 

.................................................................................. WeBPT10.20 * 

.................................................................................. WeBPT10.21 * 

Tsugawa, Sadayuki .................................................. ThAT8.7 4109 

Tsuji, Tokuo .............................................................. ThBT5.3 4392 

Tsuji, Toshio ............................................................. TuCT5.2 1717 

Tsujino, Hiroshi......................................................... ThAT1.8 3792 

Tsukahara, Atsushi................................................... TuCT5.5 1737 

.................................................................................. WeAPT10.11 * 

Tully, Stephen........................................................... MoBT2.3 482 

.................................................................................. TuBT5.7 1353 

.................................................................................. ThDT7.2 5147 

Tunnell, Robert ......................................................... TuAT3.3 894 

Turkseven, Melih ...................................................... WeAT4.5 2139 

.................................................................................. WeBPT10.11 

U 

* 

Ueda, Jun ................................................................. TuCT9.5 1940 

.................................................................................. WeAT4.5 2139 

.................................................................................. WeAPT10.23 * 

.................................................................................. WeBPT10.11 * 

Ueda, Tomohiro........................................................ ThCT3.7 4756 

Ueyama, Tsuyoshi.................................................... ThCT2.5 4698 

.................................................................................. ThDPT10.5 * 

Ugurlu, Barkan.......................................................... MoAT8.1 318 

Ulbrich, Stefan .......................................................... TuCT6.2 1761 

Ulmen, John ............................................................. ThDT5.3 5100 

Ulusoy, Alphan ......................................................... WeCT5.2 3087 

Underwood, James Patrick....................................... MoAT6.7 255 

Unluhisarcikli, Ozer................................................... ThCT7.1 4893 

Uno, Yoji ................................................................... WeBT7.4 2735 

.................................................................................. WeCPT10.19 * 

Urcola, Pablo ............................................................ WeCT1.4 2887 

.................................................................................. WeDPT10.1 

V 

* 

Vahrenkamp, Nikolaus ............................................. TuCT6.2 1761 

Vaidyanathan, Ravi .................................................. MoAT5 O 

.................................................................................. MoBT5 CC 

.................................................................................. MoBT5 O 

.................................................................................. ThAT7.3 4042 

Vallery, Heike ........................................................... WeCT4.7 3068 

Vallone, Luca............................................................ WeCT7.4 3192 

.................................................................................. WeDPT10.19 * 

Valls Miro, Jaime ...................................................... TuAT2 C 

.................................................................................. TuCT2.4 1640 

.................................................................................. WeAPT10.4 * 

Valtazanos, Aris........................................................ WeDT8.7 3679 

van den Berg, Jur ..................................................... WeBT5.7 2646 

van den Boom, Ton .................................................. WeBT7.3 2729 

Van der Loos, H.F. Machiel ...................................... WeAT1.5 1994 

.................................................................................. WeBPT10.2 * 

van der Smagt, Patrick ............................................. MoBT7 CC 

.................................................................................. MoBT7.6 672 

.................................................................................. TuAPT10.18 * 

van Rossum, Anne ................................................... ThCT4.5 4783 

.................................................................................. ThDPT10.11 * 

van Toll, Wouter ....................................................... WeDT5.5 3526 

.................................................................................. ThAPT10.11 * 

van Veen, Youri ........................................................ WeBT3.7 2557 

Vander Hook, Joshua ............................................... MoBT2.4 488 

.................................................................................. TuAPT10.4 * 

Varga, Maja .............................................................. ThCT9.6 5015 

.................................................................................. ThDPT10.27 * 

Vartholomeos, Panagiotis......................................... ThBT7.6 4508 

.................................................................................. ThCPT10.21 * 

Vasilyev, Nikolay ...................................................... TuBT5.3 1327 

.................................................................................. WeAT3.2 2083 

Vassallo, Raquel Frizera .......................................... WeDT7.6 3620 

.................................................................................. ThAPT10.18 * 

Vasseur, Pascal........................................................ ThAT6.6 4006

.................................................................................. ThBPT10.18 * 

Vaughan, Richard..................................................... WeBT8 C 

Vaz, Luís................................................................... WeBT1.6 2438 

.................................................................................. WeCPT10.3 * 

Veloso, Manuela....................................................... MoAT2.4 73 

.................................................................................. MoBPT10.4 * 

.................................................................................. WeAT8.5 2327 

.................................................................................. WeBPT10.23 * 

.................................................................................. WeDT8.1 3638 

Venture, Gentiane .................................................... WeDT9.2 3701 

.................................................................................. ThCT2.7 4710 

Veon, Kevin .............................................................. WeAT9.5 2377 

.................................................................................. WeBPT10.26 * 

Vercher, Jean-Louis ................................................. TuBT5.4 1333 

.................................................................................. TuCPT10.10 * 

Verl, Alexander......................................................... WeAT9.3 2365 

Vernaza, Paul........................................................... WeAT5.6 2186 

.................................................................................. WeBPT10.15 * 

Verschure, Paul........................................................ TuAT8.8 1115 

Verspecht, Jonathan................................................. ThBT7.1 4477 

Vial, John.................................................................. TuAT2.8 886 

Vicentini, Federico.................................................... WeCT9.7 3327 

Vidal-Calleja, Teresa A............................................. MoAT2.7 92 

.................................................................................. WeBT2.5 2489 

.................................................................................. WeCPT10.5 * 

Vijayakumar, Sethu .................................................. MoBT8.5 718 

.................................................................................. TuAPT10.20 * 

Villani, Luigi .............................................................. ThAT1.2 3752 

.................................................................................. ThBT1 CC 

.................................................................................. ThBT1.4 4194 

.................................................................................. ThCPT10.1 * 

Vincze, Markus......................................................... TuAT1.5 813 

.................................................................................. TuBT1.5 1201 

.................................................................................. TuBPT10.2 * 

.................................................................................. TuCPT10.2 * 

.................................................................................. ThCT6.5 4865 

.................................................................................. ThDPT10.17 * 

Vitiello, Valentina...................................................... TuAT5.5 949 

.................................................................................. TuBPT10.11 * 

Vogel, Christian ........................................................ WeDT1.3 3355 

Vogel, Joern ............................................................. MoBT7.6 672 

.................................................................................. TuAPT10.18 * 

Voigt, Rainer............................................................. WeBT6.6 2694 

.................................................................................. WeCPT10.18 * 

Volkhardt, Michael.................................................... WeBT1.5 2430 

Volpe, Richard.......................................................... MoBT6.1 * 

von Stryk, Oskar....................................................... ThCT5.3 4811 

Vona, Marsette ......................................................... TuBT7 C 

.................................................................................. TuBT7.6 1439 

.................................................................................. TuCPT10.18 * 

Vonthron, Manuel ..................................................... TuAT3.4 901 

.................................................................................. TuBT3.3 1285 

.................................................................................. TuBPT10.7 * 

Vorst, Philipp ............................................................ ThBT2.1 4229 

Voyles, Richard ........................................................ WeAT9.5 2377 

.................................................................................. WeBPT10.26 * 

.................................................................................. WeCT3 C 

Vrecko, Alen ............................................................. WeDT1.8 

W 

3387 

Wade, Eric................................................................ WeBT1 CC 

.................................................................................. WeBT1.1 2403 

Wagner, Glenn ......................................................... WeCT8.6 3260 

.................................................................................. WeDPT10.24 * 

Wagner, René .......................................................... WeCT9.4 3305 

.................................................................................. WeDPT10.25 * 

Wahl, Friedrich M. .................................................... WeBT2.4 2481 

.................................................................................. WeCPT10.4 * 

Walker, Ian ............................................................... TuAT8.4 1087 

.................................................................................. TuBPT10.19 * 

.................................................................................. ThAT7.6 4060 


–259– 

.................................................................................. ThBPT10.21 * 

.................................................................................. ThCT6.6 4871 

.................................................................................. ThDPT10.18 * 

Walter, Christoph...................................................... WeDT1.3 3355 

Walter, Matthew........................................................ ThBT3.4 4307 

.................................................................................. ThCPT10.7 * 

Wang, Dangxiao ....................................................... WeBT4.6 2602 

.................................................................................. WeCPT10.12 * 

Wang, Fei ................................................................. MoAT4.8 184 

Wang, Hesheng........................................................ WeCT2.7 2959 

Wang, Hongbo.......................................................... WeAT1.7 2008 

Wang, Jianxun.......................................................... MoBT5.8 588 

Wang, Keyi ............................................................... WeDT2.8 3440 

Wang, Long .............................................................. TuBT6.5 1380 

.................................................................................. TuCPT10.14 * 

Wang, Ping ............................................................... TuCT5.6 1743 

.................................................................................. WeAPT10.12 * 

Wang, Ping Chuan ................................................... ThCT6.7 4877 

Wang, Shuhui ........................................................... ThDT4.1 5061 

Wang, Tianmiao ....................................................... TuCT8.1 1863 

Wang, Weifu ............................................................. ThBT3.6 4321 

.................................................................................. ThCPT10.9 * 

Wang, Yao-Dong ...................................................... TuBT1.6 1208 

.................................................................................. TuCPT10.3 * 

Wang, Yuechao ........................................................ TuCT8.2 1869 

Wang, Yunxia ........................................................... MoAT4.8 184 

Wang, Zhikun ........................................................... MoAT8.3 332 

Wang, Zhongli .......................................................... WeCT2.7 2959 

Wang, Zhuowei......................................................... WeDT8.3 3653 

Warneken, Felix........................................................ WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Warnier, Matthieu ..................................................... WeCT1.5 2895 

.................................................................................. WeDPT10.2 * 

Warren, Frank........................................................... TuBT5.2 1321 

Waslander, Steven Lake .......................................... ThCT8.6 4961 

.................................................................................. ThDPT10.24 * 

Watanabe, Hiroki ...................................................... TuAT5.6 955 

.................................................................................. TuBPT10.12 * 

Watanabe, Kouichi ................................................... MoAT4.4 157 

.................................................................................. MoBPT10.10 * 

Watanabe, Tetsuyou ................................................ TuBT6.8 1398 

Watanabe, Wataru.................................................... TuCT8.6 1895 

.................................................................................. WeAPT10.21 * 

Weaver, Kyle D......................................................... WeBT3.1 2517 

Webster III, Robert James........................................ WeBT3.1 2517 

.................................................................................. ThAT1 C 

.................................................................................. ThAT1.4 3764 

.................................................................................. ThBPT10.1 * 

Wedge, Nathan......................................................... WeBT5.3 2620 

Wei, Gu-Yeon ........................................................... TuAT8.6 1099 

.................................................................................. TuBPT10.21 * 

Wei, Hung-Yuan ....................................................... TuCT9.8 1962 

Weinberg, Brian........................................................ ThCT7.1 4893 

Weiss, Stephan ........................................................ TuCT2.7 1661 

.................................................................................. WeAT6.7 2235 

.................................................................................. WeAT6.8 2242 

.................................................................................. WeBT6.6 2694 

.................................................................................. WeCPT10.18 * 

Weisshardt, Florian................................................... WeAT9.3 2365 

Weisz, Jonathan ....................................................... TuBT6.5 1380 

.................................................................................. TuCPT10.14 * 

Welihena Gamage, Kumudu Chalaka ThCT7.3 4905 

Gamage.................................................................... 

Wenger, Philippe ...................................................... TuBT7.8 1453 

Wettergreen, David................................................... MoBT6.4 607 

.................................................................................. MoBT6.5 613 

.................................................................................. TuAPT10.13 * 

.................................................................................. TuAPT10.14 * 

Weyers, Christopher................................................. TuAT2.4 859 

.................................................................................. TuBPT10.4 *

Whitcomb, Louis....................................................... WeDT9.5 3722 

.................................................................................. ThAPT10.23 * 

White, Paul ............................................................... MoAT9.7 408 

Whittaker, Chuck ...................................................... ThAT2.3 3816 

Whittaker, William..................................................... ThAT2.3 3816 

Wieber, Pierre-Brice ................................................. WeAT7.8 2292 

.................................................................................. ThAT3.6 3887 

.................................................................................. ThBPT10.9 * 

Williams, David......................................................... ThCT3.5 4741 

.................................................................................. ThDPT10.8 * 

Williams, Ryan.......................................................... ThBT4.2 4348 

Williams, Stefan Bernard.......................................... TuBT2.3 1242 

.................................................................................. TuBT9.4 1533 

.................................................................................. TuCPT10.22 * 

.................................................................................. ThBT6.6 4455 

.................................................................................. ThCT3.2 4722 

.................................................................................. ThCPT10.18 * 

Willimon, Bryan......................................................... ThCT6.6 4871 

.................................................................................. ThDPT10.18 * 

Wimboeck, Thomas.................................................. TuAT6.3 973 

.................................................................................. WeCT7.5 3199 

.................................................................................. WeDPT10.20 * 

.................................................................................. ThBT1.7 4215 

Windau, Jens............................................................ WeAT9.8 2397 

Winfield, Alan............................................................ ThCT9.3 4995 

Winkens, Christian.................................................... WeDT5.8 3545 

Wisse, Martijn........................................................... TuAT6.6 995 

.................................................................................. TuBPT10.15 * 

.................................................................................. ThAT5.5 3957 

.................................................................................. ThBPT10.14 * 

Witick, Martha........................................................... ThAT8.8 4115 

Wittmeier, Steffen..................................................... TuAT7.8 1063 

.................................................................................. ThAT9.5 4148 

.................................................................................. ThBPT10.26 * 

Wohlkinger, Walter ................................................... ThCT6.5 4865 

.................................................................................. ThDPT10.17 * 

Wolf, Denis Fernando............................................... TuAT2 CC 

Wolf, Michael............................................................ ThCT3.3 4728 

Wolf, Peter................................................................ WeCT4.7 3068 

Wollherr, Dirk............................................................ WeCT5.6 3114 

.................................................................................. WeDPT10.15 * 

Wong, Liang Jie........................................................ WeDT6.3 3551 

Wong, Uland............................................................. ThAT2.3 3816 

Wood, John .............................................................. WeAT8.6 2333 

Wood, Levi................................................................ ThAT4.7 3931 

Wood, Nathan........................................................... ThBT7.8 4522 

Wood, Robert ........................................................... MoAT1.6 31 

.................................................................................. MoAT9.3 384 

.................................................................................. MoAT9.8 414 

.................................................................................. MoBT5.2 * 

.................................................................................. MoBPT10.3 * 

.................................................................................. TuAT8 C 

.................................................................................. TuAT8.6 1099 

.................................................................................. TuAT8.7 1107 

.................................................................................. TuBT8.4 1479 

.................................................................................. TuBPT10.21 * 

.................................................................................. TuCT9.2 1919 

.................................................................................. TuCPT10.19 * 

.................................................................................. ThBT7.3 4488 

.................................................................................. ThCT9.2 4989 

.................................................................................. ThDT4.3 5073 

Woodward, Matthew................................................. MoBT5.3 556 

Worcester, James..................................................... ThCT4.6 4790 

.................................................................................. ThDPT10.12 * 

Wortmann, Tim......................................................... TuBT3.5 1297 

.................................................................................. TuCPT10.8 * 

Wrede, Britta............................................................. TuBT1.3 1187 

Wrede, Sebastian..................................................... WeCT3.7 3011 

Wu, Licheng.............................................................. ThDT4.1 5061 

Wu, Wenqiang.......................................................... TuBT8.3 1473 


–260– 

Wu, Yen-Chang ........................................................ WeCT2.8 2965 

Wuensche, Hans J ................................................... ThBT8.6 4562 

.................................................................................. ThCPT10.24 * 

Wurm, Kai M............................................................. ThBT2.4 4249 

.................................................................................. ThCPT10.4 * 

Wüsthoff, Tilo............................................................ TuCT9.4 1933 

.................................................................................. WeAPT10.22 * 

.................................................................................. WeDT1.5 3367 

.................................................................................. ThAPT10.2 * 

Wyeth, Gordon ......................................................... MoBT8.1 

X 

692 

Xi, Ning ..................................................................... MoAT4.6 171 

.................................................................................. MoAT4.8 184 

.................................................................................. MoBPT10.12 * 

.................................................................................. WeAT4.4 2133 

.................................................................................. WeBPT10.10 * 

Xia, Tian ................................................................... MoBT7.1 639 

Xiang, Xianbo ........................................................... WeDT6.4 3558 

.................................................................................. ThAPT10.13 * 

Xiao, Jing.................................................................. WeBT4.6 2602 

.................................................................................. WeCPT10.12 * 

.................................................................................. ThBT1 C 

.................................................................................. ThBT1.6 4207 

.................................................................................. ThCPT10.3 * 

Xiao, Junhao............................................................. TuAT1.3 801 

Xie, Dan.................................................................... WeDT1.2 3349 

Xin, Ming................................................................... MoAT7.5 292 

.................................................................................. MoBPT10.20 * 

Xu, Anqi .................................................................... ThDT3.3 5048 

Xu, De....................................................................... WeDT2.7 3434 

Xu, Kai ...................................................................... TuAT5.7 961 

Xu, Ling .................................................................... WeBT8.4 2784 

.................................................................................. WeCPT10.22 * 

Xu, Yangsheng ......................................................... TuBT8.5 1487 

.................................................................................. TuCPT10.20 

Y 

* 

Yamada, Hiroya........................................................ TuAT8.3 1081 

Yamada, Yasunori .................................................... TuBT8.7 1499 

Yamada, Yoji ............................................................ WeCT4.6 3060 

.................................................................................. WeDPT10.12 * 

Yamamoto, Akio ....................................................... ThCT1.8 4664 

Yamamoto, Kiyohito ................................................. WeAT3.7 2115 

Yamamoto, Ko.......................................................... ThCT9.7 5021 

Yamamoto, Motoji..................................................... TuCT7.6 1843 

.................................................................................. WeAPT10.18 * 

.................................................................................. ThBT4.8 4386 

.................................................................................. ThDT5 CC 

.................................................................................. ThDT6.4 5133 

Yamanishi, Yoko....................................................... TuBT3.7 1309 

Yamashita, Tsuyoshi ................................................ WeAT7.5 2274 

.................................................................................. WeAT7.6 2280 

.................................................................................. WeBPT10.20 * 

.................................................................................. WeBPT10.21 * 

Yamato, Hideaki ....................................................... WeBT2.2 2466 

Yan, Liang ................................................................ MoBT9.2 744 

Yang, Chenguang..................................................... ThAT9.1 4121 

Yang, Guang-Zhong ................................................. MoBT3.7 542 

.................................................................................. TuAT5.5 949 

.................................................................................. TuBPT10.11 * 

Yang, Guosheng....................................................... ThDT4.1 5061 

Yang, Hai.................................................................. ThDT4.2 5067 

Yang, Jianyu............................................................. WeDT2.8 3440 

Yang, Xuesong ......................................................... WeCT1.3 2879 

Yatsurugi, Manabu ................................................... MoAT9.5 395 

.................................................................................. MoBPT10.26 * 

Yazbeck, Jano .......................................................... ThAT8.6 4103 

.................................................................................. ThBPT10.24 * 

Yazdanpanah, M. J................................................... WeBT7.2 2723 

Ye, Zhou ................................................................... TuBT3.4 1291 

.................................................................................. TuCPT10.7 *

Yeh, Xiyang .............................................................. TuCT7.4 1830 

.................................................................................. WeAPT10.16 * 

Yershov, Dmitry........................................................ ThAT3.2 3862 

Yesilyurt, Serhat ....................................................... MoBT1.8 463 

Yi, Byung-Ju ............................................................. TuAT5.8 967 

.................................................................................. WeDT7.2 3595 

Yi, Seung Joon ......................................................... ThAT5.6 3963 

.................................................................................. ThBPT10.15 * 

Yim, Mark ................................................................. MoAT9.7 408 

.................................................................................. ThCT4 CC 

.................................................................................. ThCT4 O 

.................................................................................. ThCT4.1 * 

.................................................................................. ThCT4.7 4797 

Yip, Hiu Man............................................................. ThBT1.8 4222 

Yoerger, Dana .......................................................... MoAT6.8 261 

.................................................................................. ThCT3.2 4722 

Yokoi, Kazuhito......................................................... WeAT1.6 2000 

.................................................................................. WeBPT10.3 * 

.................................................................................. ThBT5 CC 

.................................................................................. ThBT5.3 4392 

.................................................................................. ThBT5.8 4428 

Yokokohji, Yasuyoshi ............................................... WeAT4 O 

.................................................................................. WeBT4 C 

.................................................................................. WeBT4 O 

.................................................................................. WeCT4 CC 

.................................................................................. WeCT4 O 

Yonezawa, Naoaki.................................................... ThBT9.5 4593 

.................................................................................. ThCPT10.26 * 

Yonezawa, Satoshi................................................... WeAT4.3 2127 

Yoo, Jae Hyun.......................................................... WeBT8.5 2790 

.................................................................................. WeCPT10.23 * 

Yoon, Han................................................................. WeAT2.8 2070 

Yoon, Hyun-Soo ....................................................... TuAT5.8 967 

Yoshida, Eiichi.......................................................... WeCT7 C 

.................................................................................. ThBT5.5 4408 

.................................................................................. ThCPT10.14 * 

Yoshida, Kazuya....................................................... MoBT6.3 601 

.................................................................................. MoBT6.7 625 

Yoshida, Morio.......................................................... WeBT1.7 2445 

Yoshida, Takami....................................................... MoBT3.4 524 

.................................................................................. TuAPT10.7 * 

Yoshida, Tomoaki..................................................... MoAT6.1 * 

Yoshida, Yuki............................................................ WeCT7.2 3179 

Yoshimoto, Kayo ...................................................... TuBT6.6 1386 

.................................................................................. TuCPT10.15 * 

Young, Diana............................................................ ThBT7.3 4488 

Yozbatiran, Nuray..................................................... TuCT5.1 1711 

Yu, Shengwei ........................................................... WeAT8.1 2300 

Yu, Wei-Shun ........................................................... TuBT8.6 1493 

.................................................................................. TuCPT10.21 * 

Yuan, Haiwen ........................................................... ThDT4.1 5061 

Yuan, Qilong............................................................. WeCT2.3 2935 

Yuan, Shuai.............................................................. WeDT8.8 3686 

Yucel, Zeynep........................................................... WeDT7.1 3589 

Yue, Tao................................................................... MoBT1.3 433 

Yuh, Junku................................................................ WeDT6.1 * 

Yuma, Matsumura .................................................... MoAT7.1 268 

Yun, Seung-kook...................................................... ThAT5 CC 

.................................................................................. ThAT5 O 

.................................................................................. ThAT5.3 3943 

Yuta, Shinichi............................................................ WeDT7.2 

Z 

3595 

Zach, Christopher..................................................... TuCT2.1 1618 

Zacharias, Franziska ................................................ TuCT6.3 1768 

Zani, Paolo ............................................................... TuCT1 C 

.................................................................................. TuCT1.6 1599 

.................................................................................. WeAPT10.3 * 

Zarzhitsky, Dimitri..................................................... ThCT3.3 4728 

Zeeshan, Arif Muhammad ........................................ TuBT3.5 1297 


–261– 

.................................................................................. TuCPT10.8 * 

Zemiti, Nabil.............................................................. WeBT3.5 2545 

.................................................................................. WeCPT10.8 * 

Zenati, Marco............................................................ TuBT5.7 1353 

.................................................................................. ThBT7.8 4522 

Zeng, Shuqing .......................................................... ThBT9.2 4575 

Zhang, Byoung-Tak .................................................. ThAT5.6 3963 

.................................................................................. ThBPT10.15 * 

Zhang, Fumin ........................................................... WeCT6 O 

.................................................................................. WeDT6 C 

.................................................................................. WeDT6 O 

.................................................................................. WeDT6.8 3583 

Zhang, Hao............................................................... WeAT2.4 2044 

.................................................................................. WeBPT10.4 * 

Zhang, Hong............................................................. TuBT2.1 1228 

.................................................................................. TuBT8.3 1473 

Zhang, Houxiang ...................................................... TuAT1.3 801 

Zhang, Jianhua......................................................... TuAT1.3 801 

Zhang, Jianwei ......................................................... TuAT1.3 801 

Zhang, Liang............................................................. MoBT9.2 744 

Zhang, Qin................................................................ TuCT5.4 1731 

.................................................................................. WeAPT10.10 * 

.................................................................................. ThAPT10.13 * 

Zhang, Xianmin ........................................................ TuBT8.3 1473 

Zhang, Xin ................................................................ WeBT4.6 2602 

.................................................................................. WeCPT10.12 * 

Zhang, Yinghua ........................................................ TuBT1.2 1180 

Zhang, Yuru.............................................................. WeBT4.6 2602 

.................................................................................. WeCPT10.12 * 

Zhao, Huijing ............................................................ WeAT2.3 2036 

Zhao, Jiangran.......................................................... TuAT5.7 961 

Zheng, Minhua.......................................................... TuAT5.7 961 

Zhou, David .............................................................. TuAT3.4 901 

.................................................................................. TuBPT10.7 * 

Zhou, Kai .................................................................. TuBT1.5 1201 

.................................................................................. TuCPT10.2 * 

.................................................................................. WeDT1.8 3387 

Zhou, Ke ................................................................... MoAT2.8 98 

.................................................................................. MoBT2.6 502 

.................................................................................. TuAPT10.6 * 

Zhou, Quan............................................................... TuAT3.5 907 

.................................................................................. TuBPT10.8 * 

Zhou, Weizhen ......................................................... TuCT2.4 1640 

.................................................................................. WeAPT10.4 * 

Zhou, Xuefeng .......................................................... TuBT8.3 1473 

Zhou, Xun ................................................................. MoAT2.8 98 

Zhu, Chun................................................................. WeDT2.1 3395 

Zhu, Haifei ................................................................ TuBT8.3 1473 

Zhu, Huayong ........................................................... MoBT5.5 568 

.................................................................................. TuAPT10.11 * 

Ziegler, Jakob ........................................................... MoAT2.6 86 

.................................................................................. MoBPT10.6 * 

Zillich, Michael .......................................................... TuAT1.5 813 

.................................................................................. TuBT1 CC 

.................................................................................. TuBT1.5 1201 

.................................................................................. TuBPT10.2 * 

.................................................................................. TuCPT10.2 * 

.................................................................................. WeDT1.8 3387 

Zinn, Michael ............................................................ ThDT7 C 

.................................................................................. ThDT7.1 5139 

Zirbel, Alex................................................................ TuCT6.8 1804 

Zlot, Robert............................................................... ThAT2 C 

.................................................................................. ThAT2.5 3830 

.................................................................................. ThBPT10.5 * 

Zufferey, Jean-Christophe ........................................ ThCT9.6 5015 

.................................................................................. ThDPT10.27 

Ž 

* 

Žlajpah, Leon............................................................ ThCT5.6 4832 

.................................................................................. ThDPT10.15 *


–262–

Keyword Index 

A 

Adaptive Control MoAT1.2, MoAT2.8, MoAT5.4, 

MoAT8.7, MoBPT10.13, MoBT7.7, 

ThAT9.1, ThBT1.1, ThCT2.7, ThCT4.2, 

TuAT9.3, TuBT1.2, TuCT2.8, TuCT8.2, 

TuCT8.4, WeAPT10.19, WeBT2.2, 

WeBT7.1, WeCT7.4, WeDPT10.19 

Aerial Robotics MoAT4.5, MoBPT10.11, MoBT5.6, 

ThAT4.5, ThAT6.6, ThAT6.7, 

ThBPT10.11, ThBPT10.18, ThBT2.5, 

ThCPT10.5, ThCT1.5, ThCT8.1, 

ThCT8.2, ThCT8.3, ThCT8.4, ThCT8.5, 

ThCT8.6, ThCT8.7, ThCT9.6, 

ThDPT10.2, ThDPT10.22, 


ThDPT10.27, ThDT4.4, ThDT6.1, 

ThDT8.1, ThDT8.2, ThDT8.3, 

TuAPT10.12, TuAT9.5, TuBPT10.23, 

TuCT2.7, WeAT6.1, WeAT6.3, 

WeAT6.4, WeAT6.5, WeAT6.6, 


WeBPT10.16, WeBPT10.17, 

WeBPT10.18, WeBT6.1, WeBT6.2, 

WeBT6.3, WeBT6.4, WeBT6.5, 


WeBT9.5, WeCPT10.16, WeCPT10.17, 

WeCPT10.18, WeCPT10.26, WeCT3.2, 

WeCT5.3 

Agent-Based Systems ThAPT10.21, ThAT4.3, ThBT9.3, 

ThCT9.2, ThCT9.7, ThDT3.2, 

WeBT4.4, WeCPT10.10, WeDT8.6 

AI Reasoning Methods ThAT9.4, ThBPT10.25, ThBT3.3, 

ThBT7.2, TuCT1.7, WeCT7.1, 

WeDT2.2, WeDT2.3 

Animation and Simulation ThBT5.6, ThCPT10.15, ThCT9.8, 

TuBT3.8, TuBT7.5, TuCPT10.17, 

WeBT4.7, WeBT5.8 

Automation in Life MoAT1.1, MoAT1.2, MoAT1.3, 

Sciences: Biotechnology, MoBT1.6, TuAPT10.3, TuAT3.7, 

Pharmaceutical and Health TuAT5.7, TuBT3.7, TuCT3.6, 

Care 

WeAPT10.9, WeAT3.3, WeAT4.4, 

WeBPT10.10, WeDT2.1 

Autonomous Agents ThAPT10.16, ThAPT10.21, ThAT4.3, 

ThAT4.5, ThAT4.6, ThAT8.6, ThAT9.4, 

ThBPT10.11, ThBPT10.12, 


ThBT8.2, ThBT9.3, ThCT9.7, TuAT7.1, 

TuBT8.7, TuCT2.8, TuCT3.6, 


WeAT8.6, WeBPT10.13, WeBPT10.24, 

WeBT5.8, WeBT6.3, WeCT5.3, 

WeDT7.4, WeDT8.1, WeDT8.6, 

WeDT8.7 

B 

Behaviour-Based Systems ThAPT10.2, ThCT9.4, ThDPT10.25, 

TuAT8.8, TuBT8.2, WeAT2.7, 

WeDT1.5 

Biologically-Inspired MoAT1.6, MoAT3.1, MoAT3.3, 

Robots 

MoAT5.1, MoAT5.2, MoAT5.4, 



MoBPT10.3, MoBPT10.13, 


MoBPT10.27, MoBT1.8, MoBT3.3, 

MoBT5.1, MoBT5.2, MoBT5.3, 


–263– 






ThAT9.8, ThBPT10.1, ThBPT10.3, 



ThBT4.5, ThBT7.3, ThCPT10.11, 


ThCT8.8, ThCT9.2, ThDPT10.3, 


ThDT4.4, TuAPT10.10, TuAPT10.11, 

TuAPT10.12, TuAT3.4, TuAT7.8, 

TuAT8.1, TuAT8.2, TuAT8.3, TuAT8.4, 

TuAT8.6, TuAT8.7, TuBPT10.7, 

TuBPT10.19, TuBPT10.21, TuBT1.3, 

TuBT8.2, TuBT8.3, TuBT8.4, TuBT8.6, 


TuCPT10.21, TuCT3.3, TuCT3.4, 

TuCT3.5, TuCT5.4, TuCT7.1, TuCT8.1, 


TuCT8.6, TuCT9.8, WeAPT10.7, 

WeAPT10.8, WeAPT10.10, 



WeAT7.6, WeAT8.8, WeBPT10.9, 

WeBPT10.20, WeBPT10.21, WeCT7.6, 

WeDPT10.21, WePL.1 

Biomimetics MoAT1.1, MoAT3.1, MoAT9.6, 


MoBT5.7, MoBT9.7, ThAT1.6, 


ThBPT10.3, ThBPT10.13, ThBPT10.20, 

ThBT4.8, ThCT3.4, ThDPT10.7, 

ThDT5.3, TuAPT10.11, TuAT8.5, 


TuBT8.1, TuCT7.2, TuCT8.3, TuCT8.4, 


WeAPT10.21, WeAT3.7, WePL.1 

Brain Machine Interface MoBT7.8, ThBT9.7, WeAT3.6, 

WeBPT10.9, WeBT1.6, WeCPT10.3, 

WeDT3.2 

C 

Calibration and 

Identification 

Cellular and Modular 

Robots 

MoAT1.7, MoBT1.5, MoBT3.4, 

ThAPT10.22, ThAPT10.23, 

ThAPT10.24, ThAT2.3, ThAT2.4, 

ThBPT10.4, ThBT4.1, ThCT2.7, 

TuAPT10.2, TuAPT10.7, TuBT3.8, 

WeAT4.4, WeBPT10.10, WeCT9.1, 

WeCT9.2, WeCT9.3, WeCT9.4, 


WeDPT10.25, WeDPT10.26, 

WeDPT10.27, WeDT9.1, WeDT9.2, 



MoAT9.7, ThAPT10.19, ThBT4.5, 


ThDPT10.11, WeCT5.7, WeDT8.3, 

WeDT8.4 

Climbing robots ThAT8.5, ThBPT10.23, ThBT2.8, 

ThDT5.1, ThDT5.3, ThDT5.4, ThDT6.4, 


Cognitive Human-Robot 

Interaction 

WeBT7.7 


ThAPT10.3, ThBT9.7, TuAT1.7,

TuAT5.3, TuBT1.5, TuBT1.8, TuBT9.1, 

TuCPT10.2, TuCT5.8, WeAT1.3, 




WeCPT10.10, WeCT1.2, WeCT1.5, 



WeDT3.3 

Collision Avoidance MoAT6.6, MoBPT10.18, ThAT3.1, 





ThDPT10.23, TuCT1.1, TuCT1.2, 


TuCT1.7, TuCT2.1, TuCT9.7, 

WeAPT10.1, WeAPT10.2, WeAPT10.3, 

WeAT8.7, WeBT4.5, WeBT5.4, 



Compliance and 

Impedance Control 

WeDT8.2 




ThBT1.8, ThBT4.3, ThCT1.4, 

ThDPT10.1, TuAPT10.20, TuAT6.3, 



WeCT3.5, WeCT7.5, WeDPT10.8, 

WeDPT10.20 

Compliant Assembly MoBT9.4, TuAPT10.22 

Computer Vision MoAT1.2, MoAT1.4, MoBPT10.1, 

MoBT3.7, ThAPT10.6, ThAPT10.18, 





ThBT6.3, ThBT6.4, ThBT6.5, ThBT6.6, 


ThCPT10.17, ThCPT10.18, ThCT5.4, 




ThDPT10.17, ThDPT10.18, TuAT1.1, 






TuBT2.8, TuBT7.6, TuBT9.3, 

TuCPT10.1, TuCPT10.2, TuCPT10.3, 


TuCT2.6, WeAPT10.3, WeAPT10.6, 





WeBPT10.4, WeBPT10.7, WeBPT10.8, 


WeBPT10.27, WeBT9.7, WeCT2.2, 



WeDPT10.6, WeDPT10.27, WeDT1.1, 


WeDT7.6 

Contact Modelling MoAT7.3, ThAT9.2, ThBT1.8, ThBT7.1, 


TuCPT10.15, TuCPT10.17, 

TuCPT10.18, WeBT4.7 


–264– 

Control Architectures and 

Programming 

MoBT9.1, ThAPT10.18, ThAT3.6, 

ThBPT10.9, ThCT2.3, TuAT7.1, 

TuAT7.2, TuAT7.3, TuAT7.5, 

TuBPT10.17, WeAT9.8, WeCT5.7, 

WeDT7.6 

Cooperating Robots MoAT2.8, MoBT2.1, ThAPT10.16, 

ThAPT10.20, ThBT2.8, ThBT4.2, 



ThDPT10.26, ThDPT10.27, ThDT3.2, 




WeBPT10.22, WeBPT10.23, WeBT1.8, 






Cooperative Manipulators MoAT6.6, MoBPT10.18, ThBT1.3, 

TuBT7.1 

D 

Demining Systems TuAT9.7 

Dexterous Manipulation ThAT1.2, ThAT7.5, ThAT7.7, 

ThBPT10.20, ThBT1.4, ThCPT10.1, 

ThDT6.1, TuBT6.7, TuBT7.4, TuBT7.7, 

TuBT8.1, TuCPT10.16, TuCT6.2, 

TuCT6.7, TuCT6.8, WeBT3.1, 

WeCT2.5, WeDPT10.5 

Distributed Robot Systems MoAT3.5, MoAT4.5, MoBPT10.8, 

MoBPT10.11, MoBT2.1, ThAPT10.19, 

ThAT4.6, ThBPT10.12, ThBT1.1, 








ThDPT10.27, ThDT8.4, TuAT7.3, 





WeBT8.8, WeCPT10.24, WeCT4.2, 



Domestic Robots and 

Home Automation 



ThDPT10.26, TuAT1.2, TuAT1.4, 

TuAT2.1, TuBPT10.1, TuBT1.5, 

TuBT1.8, TuCPT10.2, WeAT1.1, 

WeAT9.3, WeBT1.5, WeCPT10.2, 


WeCT3.7, WeDPT10.3, WeDPT10.4, 

WeDPT10.7 

Dynamics MoAT7.3, MoAT7.5, MoAT7.6, 

MoAT7.7, MoBPT10.20, MoBPT10.21, 





ThBPT10.26, ThCT4.3, ThDT8.2, 

TuAPT10.12, TuAT8.7, TuBT7.5, 

TuCPT10.17, TuCT7.2, WeAT7.1, 

WeAT7.4, WeBPT10.19, WeBT6.5, 


WeCPT10.17, WeCPT10.20, 

WeCPT10.21, WeDT9.2, WeDT9.6 

E

Education Robotics WeBT1.2 

Entertainment Robotics MoAT3.4, MoBPT10.7, ThDT6.1, 


WeBPT10.3, WeCT1.8 

Evolutionary Robotics ThBT4.4, ThCPT10.10, ThCT9.3, 

WeAT7.7, WeBT8.7 

F 

Failure Detection and WeAT8.3, WeCT3.7 

Recovery 

Field Robots MoAT6.1, MoAT6.3, MoAT6.4, 






ThAT8.4, ThAT8.5, ThBPT10.5, 







TuAPT10.21, TuCT1.3, TuCT7.7, 

WeAT6.3, WeAT9.1, WeBT2.5, 




WeDT9.7 

Flexible Arms MoAT9.2, ThAT1.4, ThAT3.8, ThAT7.2, 

ThBPT10.1, ThBT1.2, ThBT1.6, 

ThBT1.7, ThCPT10.3, ThCT1.6, 

ThDPT10.3, ThDT7.1, TuAT8.5, 

TuBPT10.20, TuBT6.3, TuCT6.6, 



WeAPT10.18, WeBT3.6, WeCPT10.9, 

WeDT9.3 

Force and Tactile Sensing MoAT1.6, MoAT1.7, MoAT7.4, 

MoBPT10.3, MoBPT10.19, ThAPT10.7, 



ThCPT10.23, ThCT2.4, ThDPT10.4, 

TuAT5.2, TuBT9.7, TuCT9.2, TuCT9.3, 

TuCT9.6, WeAPT10.24, WeAT4.5, 


WeBT4.5, WeCPT10.11, WeDT3.1, 


WeDT3.7, WeDT9.1, WeDT9.7 

Force Control MoAT7.6, MoAT9.4, MoBPT10.21, 

MoBPT10.25, MoBT5.4, ThAT1.3, 

ThBT1.2, ThBT1.8, ThCT7.1, 




WeCT7.2 

G 

Gesture, Posture, Social 

Spaces and Facial 

Expressions 

MoAT2.6, MoBPT10.6, ThAPT10.2, 


WeBPT10.3, WeDT1.5 

Grasping MoAT8.8, ThAPT10.7, ThBT1.6, 

ThCPT10.3, ThCT8.8, TuAT6.1, 











–265– 

Haptics and Haptic 

Interfaces 

Human detection & 

tracking 

Human Performance 

Augmentation 

Humanoid and Bipedal 

Locomotion 

WeAPT10.14, WeBT6.2, WeCT2.5, 

WeDPT10.5, WeDT3.4 

H 

MoAT4.3, MoAT4.7, MoBT7.2, 


ThAPT10.8, ThCT1.8, TuAPT10.16, 














WeDT9.8 

MoAT3.8, MoBT3.6, ThAPT10.5, 

ThAPT10.16, ThAPT10.17, ThAT2.6, 

ThAT2.7, ThBPT10.6, TuAPT10.9, 

WeAT2.3, WeBT1.4, WeCPT10.1, 






WeDT7.8 

MoAT9.1, ThAPT10.1, ThBT8.5, 

ThCPT10.23, TuBT5.4, TuCPT10.10, 


WeCT1.8, WeDT1.4, WeDT3.7 

MoAT8.1, ThAT5.1, ThAT5.3, ThAT5.4, 





ThCT5.1, ThCT5.3, ThDT5.2, TuBT8.2, 

TuCT5.3, TuCT7.1, WeAT7.1, 






Humanoid Robots MoBT7.8, ThAPT10.9, ThAT3.6, 






ThBT5.6, ThBT5.8, ThBT6.7, 

ThCPT10.13, ThCPT10.14, 





TuAT7.7, TuBT1.1, TuBT9.3, TuCT1.4, 



WeBPT10.3, WeCT1.2, WeCT1.7, 





Hydraulic/Pneumatic 

Actuators 

WeDT3.6 


ThBT7.3, ThCT5.7, TuAPT10.16, 

TuAPT10.22, TuBT5.5, TuCPT10.11,



I 

Industrial Robots MoBT9.1, ThAT1.7, ThAT7.2, ThBT9.8, 





Intelligent Transportation 

Systems 

WeCPT10.4, WeCT2.2, WeCT8.8 

ThAT3.4, ThBPT10.7, ThBT6.4, 


ThBT9.6, ThBT9.7, ThBT9.8, 

ThCPT10.16, ThCPT10.25, 

ThCPT10.26, ThCPT10.27, TuCT1.6, 


WeCT8.8, WeDT8.8 

Intrusion Detection, MoBT3.6, TuAPT10.9 

Identification and Security 

J 

Joint/Mechanism MoBT9.2, MoBT9.6, ThAPT10.24, 

ThAT1.7, ThAT7.3, ThCT2.6, 



TuBPT10.11, TuBT6.3, TuBT7.3, 


WeAPT10.16, WeAPT10.17, WeBT7.5, 

WeCPT10.20, WeDT9.6 

K 

Kinematics MoAT5.8, MoAT7.5, MoAT9.5, 

MoBPT10.20, MoBPT10.26, MoBT1.8, 





ThDT3.1, TuAT8.5, TuBPT10.20, 


TuCT9.1, WeAT3.1, WeCT2.1, 

WeCT7.8, WeDT5.4 

L 

Learning and Adaptive MoAT8.1, MoAT8.2, MoAT8.3, 

Systems 









TuAPT10.11, TuAPT10.18, 






TuCPT10.1, TuCPT10.24, TuCT8.1, 





WeBPT10.27, WeCT1.2, WeCT3.2, 




Legged Robots MoAT5.3, MoAT5.7, MoBT5.4, 





ThDT5.1, ThDT6.3, TuAPT10.10, 



–266– 






WeBT7.7, WeBT7.8, WeCPT10.6, 

WeCPT10.19, WeCPT10.21, WeCT7.7 

Localization MoAT2.1, MoAT2.2, MoAT2.3, 



MoAT3.7, MoAT3.8, MoBPT10.4, 

MoBPT10.5, MoBPT10.6, MoBPT10.8, 





ThBT3.1, ThBT7.7, ThCT2.4, ThCT2.5, 



TuAPT10.6, TuAPT10.14, TuBT2.2, 

TuBT5.7, TuCT2.3, TuCT2.6, 

WeAPT10.6, WeAT2.5, WeBPT10.5, 




WeCT8.4, WeDPT10.22, WeDT6.8 

M 

Manipulation and 

Compliant Assembly 

ThAPT10.2, ThBT1.6, ThCPT10.3, 

ThDT6.2, TuCT3.7, TuCT6.6, 

WeAPT10.15, WeDT1.5 

Manipulation Planning MoBT9.8, ThBT1.5, ThBT3.4, 


ThCT1.3, TuAT6.1, TuAT6.6, TuAT6.8, 




WeAPT10.13, WeAT1.3, WeCT7.1 

Mapping MoAT2.2, MoAT6.3, MoBT3.4, 






ThBT6.5, ThCPT10.4, ThCPT10.5, 


ThPT11.1, TuAPT10.7, TuAT2.2, 




WeBT2.8, WeCT2.5, WeCT8.5, 

WeDPT10.5, WeDPT10.23, WeDT9.4 

Marine Robotics MoAT6.8, MoBT5.8, ThAPT10.13, 


ThAPT10.23, ThCT3.2, ThCT3.3, 


ThDPT10.7, ThDPT10.8, ThDPT10.9, 

ThDT3.2, ThDT3.3, ThDT3.4, TuAT9.4, 

TuAT9.6, TuBPT10.22, TuBPT10.24, 







WeDT9.5 

Mechanism Design MoAT1.8, MoAT3.3, MoAT5.5, 




ThAT7.4, ThBPT10.19, ThBT7.5,

Medical Robots and 

Systems 








TuPL.1, WeAPT10.16, WeAPT10.17, 

WeAPT10.18, WeAPT10.23, WeAT4.7, 


WeCPT10.21 


MoBT9.3, ThBT7.5, ThBT7.6, ThBT7.7, 







TuBPT10.7, TuBPT10.10, TuBPT10.11, 

















Micro-manipulation MoAT1.3, MoAT1.5, MoBPT10.2, 



ThAT7.7, ThDT7.4, TuAPT10.1, 

TuAPT10.2, TuAPT10.3, TuAT3.3, 





TuCT3.8 

Micro/Nano Robots MoAT1.1, MoAT1.3, MoAT1.4, 



MoBT1.8, ThAT3.7, ThAT4.8, ThAT6.1, 


ThDT4.3, TuAT3.1, TuAT3.4, TuAT3.5, 





TuBT7.3, TuCPT10.7, TuCPT10.8, 



TuCT3.8, WeAPT10.7, WeAPT10.8, 

WeAPT10.9 

Mining Robotics MoBT8.6, ThBT2.2, ThBT9.6, 

ThCPT10.27, TuAPT10.21 

Mobile Manipulation MoAT8.8, ThAPT10.7, ThBT2.4, 




TuBPT10.16, TuBT3.4, TuCPT10.7, 




–267– 

WeDPT10.20, WeDT3.4 

Motion and Path Planning MoBT6.3, MoBT9.8, ThAPT10.10, 










ThCT3.3, ThCT8.3, ThDT3.3, ThDT5.2, 

ThDT7.3, ThDT8.3, TuAT2.1, TuAT9.2, 



























Motion Control MoAT5.3, MoAT6.6, MoAT7.2, 








ThBT1.3, ThBT4.8, ThBT5.7, ThCT2.2, 

ThCT5.6, ThDPT10.15, ThDT7.4, 


TuAPT10.19, TuAPT10.21, TuAT8.4, 

TuBPT10.19, TuCT5.7, TuCT8.1, 


WeAT7.8, WeAT8.8, WeBT2.2, 





WeDPT10.11, WeDT8.2, WeDT8.5 

Multifingered Hands ThBT1.4, ThBT1.5, ThCPT10.1, 

ThCPT10.2, TuAT6.3, TuBT6.3, 




TuCT6.5, WeAPT10.13, WeAPT10.14 

N 

Navigation MoAT2.2, MoAT2.7, MoAT6.8, 



ThAT6.5, ThBPT10.7, ThBPT10.10,



ThCT8.3, ThCT8.7, ThCT9.1, TuAT2.2, 


TuBPT10.4, TuCT1.3, TuCT2.5, 







Networked Robots ThAPT10.20, ThBT4.6, ThCPT10.12, 


ThDT8.4, TuAT2.3, TuAT2.7, WeAT8.1, 



WeCT8.4, WeDPT10.22, WeDT8.2, 

WeDT8.5 

Networked Teleoperation MoAT4.4, MoAT4.6, MoAT4.7, 



WeCT4.2, WeCT4.3 

Neural and Fuzzy Control ThBT7.2, TuAT2.7, WeBT8.7, 

WeCT1.1 

Neurorobotics TuBT8.7, TuCT5.2, TuCT5.4, 

WeAPT10.10 

New Actuators for Robotics MoAT9.2, MoAT9.7, MoBT9.2, 



ThBPT10.19, ThCT2.6, ThDPT10.6, 

Nonholonomic Motion 

Planning 

TuAPT10.22 

ThAPT10.10, ThAPT10.12, ThBT3.5, 

ThBT3.8, ThCPT10.8, WeAT5.4, 


WeBT9.1, WeDT5.1, WeDT5.4, 

WeDT5.6 

O 

Omnidirectional Vision MoBT2.3, MoBT2.5, ThBT2.1, 

ThCT8.2, TuAPT10.5 

P 

Parallel Robots ThAT7.2, ThBT7.5, ThCPT10.20, 

ThCT7.5, ThDPT10.20, ThDT4.2, 

TuBT7.8, TuCT7.6, WeAPT10.18, 


WeCT4.7 

Parts Feeding and TuBT6.7 

Fixturing 

Path Planning for 

Manipulators 

Path Planning for Multiple 

Mobile Robots or Agents 

ThAT3.8, ThBT3.4, ThCPT10.7, 


ThDPT10.5, TuBT7.8, WeAT5.5, 


WeBT5.6, WeCPT10.15, WeDT5.3 

MoBT1.6, ThAPT10.12, ThBT9.8, 

ThCT9.7, TuAPT10.3, TuCT1.1, 








Personal Robots MoAT7.1, ThBT2.6, ThCPT10.6, 

ThCT6.3, ThCT6.7, WeAT1.4, 


Physical Human-Robot 

Interaction 

WeCPT10.2 


MoBPT10.24, ThAPT10.1, ThAPT10.9, 

ThAT7.1, ThCT1.7, ThCT1.8, ThCT7.1, 


ThDPT10.19, ThDPT10.20, TuCT5.5, 


–268– 

Planning, Scheduling and 

Coordination 














TuBPT10.22, TuBPT10.23, WeAT1.1, 

WeBT8.8, WeCT8.6, WeDPT10.24, 


Programming Environment TuAT7.3, TuAT7.6, TuBPT10.18, 

WeCT7.1 

R 

Range Sensing MoAT9.8, ThAT2.2, ThAT2.3, ThAT2.5, 




ThDPT10.17, TuAT1.8, TuBT2.4, 



WeAT2.1, WeCT9.5, WeDPT10.26, 

Reactive and Sensor- 

Based Planning 

WeDT7.1 

MoBT8.7, ThAPT10.15, ThCT8.5, 

ThCT8.7, ThDPT10.23, TuAT9.7, 

WeBT1.7, WeBT2.8, WeCT5.1, 



Recognition MoBT3.5, ThAT2.1, ThAT2.8, ThCT5.4, 



ThDPT10.17, TuAPT10.8, TuAT1.1, 






TuCPT10.23, WeAT2.2, WeAT2.4, 






Redundant Robots MoAT5.8, MoBT7.5, MoBT7.7, 


ThAT9.8, ThBT1.3, TuAPT10.17, 


TuCT1.8, WeBT7.7, WeBT7.8, 

WeCT7.4, WeCT7.8, WeDPT10.19 

Rehabilitation Robotics MoBT7.6, ThAT5.8, ThBT1.1, ThBT7.2, 




ThDPT10.21, TuAPT10.18, TuBT5.2, 




WeAPT10.12, WeBT1.1, WeBT1.4, 

Robot Companions and 

Social Human-Robot 

Interaction 

WeCPT10.1, WeCT4.8, WeDT9.2 

ThAPT10.17, WeAT1.4, WeAT1.8, 





WeDT7.5

Robot Safety MoAT6.4, MoBPT10.16, ThAT8.1, 

ThBT3.8, ThBT9.1, TuCT7.8, 




Robotic Software, 

Middleware and 

Programming 

Environments 

WeDPT10.15 

ThAPT10.19, ThCT2.3, ThCT4.4, 



TuBPT10.16, TuBPT10.17, 

TuBPT10.18, TuCT6.2, WeAT2.7, 

WeDT8.4 

ThBT2.5, ThCPT10.5, TuBT8.3, 

TuBT8.5, TuCPT10.20, WeAT9.1 

Robotics in Agriculture and 

Forestry 

Robotics in Construction ThAT8.2, ThCT4.8, WeDT9.7 

Robotics in Hazardous MoAT5.6, MoAT6.4, MoAT6.5, 

Fields 


MoBPT10.17, ThCT4.7 

S 

Search and Rescue MoAT5.6, MoBPT10.15, MoBT8.7, 

Robots 



TuAT8.3, TuCT2.4, TuCT8.5, TuCT8.8, 

WeAPT10.4, WeAPT10.20, WeBT2.3, 

WeDT1.2 

Sensor Fusion MoAT2.3, MoAT3.8, MoAT6.7, 










TuCT2.1, TuCT9.7, WeAT2.5, 







WeCT9.3, WeCT9.8, WeDT2.6, 

WeDT7.8 

Sensor Networks MoAT3.5, MoBPT10.8, MoBT2.6, 

ThAPT10.5, ThAPT10.14, ThDT8.4, 

TuAPT10.6, TuAT2.8, TuCT9.2, 






Service Robots ThAPT10.18, ThBT1.2, ThCT6.6, 



TuBT9.1, WeAT2.7, WeAT9.3, 


WeDT7.6 

SLAM MoAT2.1, MoAT2.3, MoAT2.7, 

MoBT3.4, MoBT6.8, ThAPT10.22, 




ThDT7.2, ThPT11.1, TuAPT10.7, 








–269– 


WeAPT10.4, WeAPT10.5, WeAPT10.6, 



Smart Actuators MoAT1.8, MoAT9.3, MoAT9.6, 


MoBT9.7, TuBT8.8, TuCT9.5, 

WeAPT10.23 

Sonars WeCT1.7, WeCT6.7, WeDT6.7 

Space Robotics MoBT6.1, MoBT6.2, MoBT6.3, 


MoBT6.7, MoBT6.8, ThBT6.2, 

ThCT1.6, ThDPT10.3, TuAPT10.13, 

TuAPT10.14, TuAPT10.15, WeAT4.8, 


Surveillance Systems ThBT8.2, ThCT8.4, ThDPT10.22, 





WeDT7.3 

T 

Telerobotics MoAT4.1, MoAT4.3, MoAT4.4, 





MoBT7.5, ThAT8.8, ThBT7.1, ThCT3.7, 

ThCT4.7, TuAPT10.2, TuAPT10.16, 




WeCPT10.7, WeCT4.3 

Tendon/Wire Mechanism MoBT5.7, MoBT9.5, ThBT1.7, 

ThCT8.8, TuAPT10.23, TuAT5.2 

U 

Ubiquitous Robotics ThAPT10.5, WeDT2.5 

Underactuated Robots MoBT6.4, ThAT3.7, ThAT7.6, ThAT9.5, 



ThDT5.2, TuAPT10.13, TuBT6.4, 




WeBT7.8, WeCPT10.16, WeCPT10.19 

V 

Virtual Reality and 

Interfaces 



ThCT1.8, TuAPT10.8, TuBT5.3, 



WeCPT10.7, WeCPT10.12, WeDT3.3, 

WeDT3.5 

Visual Learning ThAT6.3, ThBT6.1, ThCT1.3, ThCT6.4, 






WeAT9.7, WeBPT10.6, WeDT1.8 

Visual Navigation MoAT2.1, MoAT2.5, MoAT6.7, 

MoBPT10.5, MoBT2.5, ThAT6.4, 



ThBT6.8, ThCPT10.18, ThDT3.3, 

ThPT11.1, TuAPT10.5, TuBT2.1, 




WeAPT10.2, WeAPT10.4, WeAPT10.5,




Visual Servoing MoBT6.6, MoBT9.6, ThAT6.1, 


ThCT8.1, TuAPT10.15, TuAPT10.24, 







Visual Tracking MoAT1.4, MoAT2.5, MoAT3.4, 








ThDPT10.22, ThDT8.1, TuAPT10.15, 

TuAT3.3, TuBT1.6, TuBT3.5, TuBT7.4, 





–270– 

Voice, Speech Synthesis 

and Recognition 










TuAPT10.8, TuAPT10.9, WeCT1.6, 

WeDPT10.3, WeDT1.1

Alan is looking for new colleagues! 

Bosch Research and Technology Center 

www.boschresearch.com

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Service robots. Human-robot interaction. Edutainment. 

The age of service robotics has long since dawned. 

In close cooperation with renowned universities and institutes 

around the world, KUKA Laboratories is developing new 

solutions and setting new technological trends. Already 

now, the product portfolio of KUKA Laboratories ranges from 

robotics technologies for medical applications to the entertainment 

sector; from motion simulators to technologies for 

patient positioning or medical imaging; and from production 

assistants that collaborate with humans to reference products 

for research and education. KUKA Laboratories is shaping the 

age of service robotics with its high-tech solutions. 

Capture this QR Code with your mobile phone 

and learn more about KUKA Laboratories: 

www.kuka-labs.com

Redefining the Design 

Space for Robotics 

• Taurus fieldable dexterous telemanipulation 

• Wall climbing robots and new grasping solutions 

• KARTO mapping capability 

• And more… 

About SRI International 

Visit Us in the 

Exhibit Area 

to Learn about 

Our Latest 

Robotics 

Innovations 

Silicon Valley-based SRI International, a nonprofit research and development 

organization, performs sponsored R&D for governments, businesses, and 

foundations. SRI brings its innovations to the marketplace through technology 

licensing, new products, and spin-off ventures. Commemorating its 65th 

anniversary in 2011, SRI is known for world-changing innovations in computing 

and robotics, health and pharmaceuticals, chemistry and materials, sensing, 

energy, education, and national defense. 

www.sri.com/robotics

Transform your workforce with ABB robotics. 

Jumpstart your business with ABB robotics. 

ABB Inc. 

Discrete Automation and Motion 

Robotics 

Auburn Hills, MI 

248-391-9000 

The reality of the current business climate presents an 

incredible opportunity. Make good decisions now and you will 

emerge as a leader in your industry. With the most versatile 

and extensive lineup of top performing robots available, ABB 

will give you the automation edge to outsmart the competition. 

Examples include picking, packing and palletizing robots with 

better cycle-times and higher payloads, FlexArc® welding 

systems, precise, flexible and safe foundry robots for forging, 

casting, machining and material removal, high-speed, compact 

robots to assemble delicate solar cells; and large, long-reach 

robots to handle complete solar modules. 

ABB Robotics - the competitive edge you need! 

www.abb.com/robotics

It’ll only coSt you An ARm. 

Announcing the PR2 SE for under $200,000. 

As you can see, there’s something quite distinct about this 

model. The PR2 SE is a single-armed robot with an updated 

sensor suite. 

Particularly in light of such new programs as the NSF 

National Robotics Initiative, we’ve been encouraged to offer 

our personal robot platform at a more affordable price. The 

PR2 SE is priced at $285,000 but a 30% discount is offered 

to individuals with a proven track record of contributions to 

the open source community. 

While it doesn’t enable two-handed tasks, the PR2 SE is able 

to leverage the work of the PR2 community on navigation, 

perception, manipulation and grasping. And if you find 

you need a second arm, you can add one later. For more 

information, email us at pr2info@willowgarage.com or visit 

willowgarage.com. 

©2011 Willow Garage.

Join the 450 prestigious 

universities already in the 

NAO community ! 

www.aldebaran-robotics.com 

Intuitive Surgical leads in the development and commercialization of medical robotic technology. The da Vinci ® Surgical System 

enables surgeons to perform delicate and complex operations minimally-‐invasively, with increased vision, precision, dexterity and 

control. State-‐of-‐the-‐art robotic technology allows the surgeon’s hand movements to be scaled, filtered and translated into precise 

movements of the instruments working inside the patient’s body. The end result: a breakthrough in surgical capabilities. 

Taking surgical precision beyond the limits of the human hand 

SOON 

OPEN SOURCE 

americas@aldebaran-robotics.com

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SCHUNK_Anzeige_Servicerobotik_215x139_4C_2011.indd 1 26.08.2011 15:07:26 

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IROS 2011 Awards - Lirmm

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