Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
- TAGS
- pegaso
- october
- lirmm
- www2.lirmm.fr
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
<strong>Session</strong> WedFVT9 Fenix 1 <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 16:15–17:30<br />
Multi-modal Learning II<br />
Chair<br />
Co-Chair Shingo Shimoda, RIKEN<br />
16:15–16:30 WedFVT9.1<br />
Experimental study on haptic communication of<br />
a human in a shared human-robot collaborative<br />
task<br />
Julie Dumora, Franck Geffard, Catherine Bidard<br />
LIST, CEA, France<br />
Thibaut Brouillet Philippe Fraisse<br />
EPSYLON, Montpellier, France LIRMM, Montpellier, France<br />
• Robot assistances and operator intention<br />
detection proposed to overcome<br />
limitations of backdrivable robot in long<br />
objects manipulation<br />
• The solution of analysing haptic cues to<br />
tackle the rotation/translation ambiguity is<br />
proposed<br />
• Relationships between operator intention<br />
of motion and haptic measurements are<br />
highlighted<br />
• Wrench measurements are shown to be<br />
an incomplete information for detection of<br />
operator’s intention of motion<br />
Rotation/translation ambiguity<br />
in joint human-robot manipulation<br />
of a long object<br />
(view from top down)<br />
16:45–17:00 WedFVT9.3<br />
Maximally Informative Interaction<br />
Learning for Scene Exploration<br />
Herke van Hoof, Oliver Kroemer, Heni Ben Amor and Jan Peters<br />
FG Intelligent Autonomous Systems, TU Darmstadt, Germany<br />
Max Planck Institute for Intelligent Systems, Germany<br />
• In dynamic environments, robots<br />
need to handle novel objects.<br />
• As annotated data is absent robots<br />
need to learn from the results of<br />
their actions.<br />
• Exploratory actions that maximize<br />
information gain allow more efficient<br />
learning.<br />
• This method allows a robot to<br />
efficiently learn with minimal prior<br />
information.<br />
17:15–17:20 WedFVT9.5<br />
Towards Robotic Calligraphy<br />
Nico Huebel, Elias Mueggler, Markus Waibel,<br />
and Raffaello D’Andrea<br />
Institute for Dynamic Systems and Control,<br />
ETH Zurich, Switzerland<br />
• We present a prototype of a robotic system that learns how to draw<br />
Chinese characters.<br />
• First, the context of the project is presented.<br />
• Then, the experimental setup and the overall approach is introduced.<br />
• Finally, experimental results are presented and discussed.<br />
This is the experimental setup of our prototype consisting of the<br />
KUKA Light Weight Robot, a Prosilica GC 655C camera, and a brush.<br />
16:30–16:45 WedFVT9.2<br />
Robots Move: Bootstrapping the Development of<br />
Object Representations using Sensorimotor<br />
Coordination<br />
Arren Glover and Gordon Wyeth<br />
Queensland University of Technology, Australia<br />
• This paper is concerned with the<br />
unsupervised generation of object<br />
models by fusing appearance and<br />
action<br />
• A FAB-MAP-based approach is<br />
combined with a partially<br />
observable semi-Markov decision<br />
process<br />
• Results indicate stronger bag-ofword<br />
object representations are<br />
formed under sensorimotor<br />
constraints<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–182–<br />
Representations are matched via<br />
self-motion prediction to become<br />
object specific<br />
17:00–17:15 WedFVT9.4<br />
Perceptual Development Triggered<br />
by its Self-Organization in Cognitive Learning<br />
Yuji Kawai, Yukie Nagai, and Minoru Asada<br />
Graduate School of Engineering, Osaka University, Japan<br />
• Our goal: To investigate the role of<br />
visual development triggered by selforganization<br />
in the visual space in a<br />
case of the mirror neuron system (MNS)<br />
• Key idea: The self-triggered visual<br />
development changes adaptively the<br />
developmental speed.<br />
• Result: The self-triggered development<br />
maintains a long enough period of<br />
immature vision which can inhibit selfother<br />
differences in observation. Thus<br />
the development enhances the<br />
acquisition of the association between<br />
self and other (i.e., the MNS).<br />
(a) Early stage<br />
of development<br />
(b) Later stage<br />
of development<br />
17:20–17:25 WedFVT9.6<br />
Learning Throwing and Catching Skills<br />
Jens Kober, Katharina Muelling, and Jan Peters<br />
AGBS, MPI for Intelligent Systems, Germany<br />
IAS, TU Darmstdat, Germany<br />
• Learning hitting skills by imitation and reinforcement<br />
learning<br />
• Generalizing hitting skills to catching skills<br />
• Learning to throw at targets<br />
• Combining throwing and catching skills to play catch<br />
A BioRob and a Barrett WAM playing catch.