Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedCT7 Vega Wednesday, October 10, 2012, 11:00–12:30
Robot Interaction with the Environment and Humans
Chair Li-Chen Fu, National Taiwan Univ.
Co-Chair Jan Peters, Tech. Univ. Darmstadt
11:00–11:15 WedCT7.1
A Brain-Robot Interface for Studying Motor
Learning after Stroke
Timm Meyer 1 , Jan Peters 1,2 , Doris Brötz 3 ,
Thorsten O. Zander 1 , Bernhard Schölkopf 1 ,
Surjo R. Soekadar 3 , Moritz Grosse-Wentrup 1
1 Max Planck Institute for Intelligent Systems, Germany
2 Intelligent Autonomous Systems Group,
Technische Universität Darmstadt, Germany
3 Institute of Medical Psychology and Behavioural Neurobiology,
University of Tübingen, Germany
• System:
Combining robotics and EEG to study
neural correlates of motor learning after
stroke
• Pilot study:
Virtual 3D reaching movements with
stroke patients
• Results:
Pre-trial bandpower in contralesional
sensorimotor areas may be a neural
correlate of motor learning.
Subject wearing an EEG-cap while
being attached to the robot arm
11:30–11:45 WedCT7.3
Haptic Classification and Recognition of Objects
Using a Tactile Sensing Forearm
Tapomayukh Bhattacharjee, James M. Rehg, and
Charles C. Kemp
Center for Robotics and Intelligent Machines,
Georgia Institute of Technology, USA
• Method:
- PCA on concatenated time series
- k-NN on top components
• Leave-one-out cross-validation accuracy
- Fixed vs. Movable: 91%
- 4 Categories: 80%
•(Fixed, Movable) X (Soft, Rigid)
- Recognize which of 18 objects: 72%
• Limitations
- Stereotyped motion of the arm
- Single contact region
12:00–12:15 WedCT7.5
Using a Minimal Action Grammar for
Activity Understanding in the Real World
Douglas Summers-Stay, Ching L. Teo, Yezhou Yang, Cornelia
Fermuller and Yiannis Aloimonos
Commputer Science, University of Maryland College Park, USA
• We have built a system to
automatically build an activity tree
structure from observations of an actor
performing complex manipulation
activities
• We created a dataset of these
activities using Kinect RGBD and
SR4000 time-of -light cameras.
• The grammatical structure used to
understand these actions may provide
insight into a connection between
action and language understanding
• Activities recognized include
assembling a machine, making a
sandwich, creating a valentine card,
etc…
By noting key moments when
objects come together, we build a
tree for activity recognition
11:15–11:30 WedCT7.2
A brain-machine interface to navigate mobile
robots along human-like paths amidst obstacles
Abdullah Akce, James Norton
University of Illinois at Urbana-Champaign, USA
Timothy Bretl
University of Illinois at Urbana-Champaign, USA
• We present an interface that
allows a human user to specify a
desired path with noisy binary
inputs obtained from EEG
• Desired paths are assumed to be
geodesics under a cost function,
which is recovered from existing
data using structured learning
• An ordering between all (local)
geodesics is defined so that users
can specify paths optimally
• Results from human trials
demonstrate the efficacy of this
approach when applied to a
simulated robotic navigation task
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–144–
The interface provides feedback by displaying
an estimate of the desired path. The user gives
left/right inputs based on “clockwise” ordering
of the desired path to the estimated path.
11:45–12:00 WedCT7.4
Proactive premature intention estimation for
intuitive human-robot collaboration
Muhammad Awais and Dominik Henrich
Chair for Applied Computer Science III,
University of Bayreuth, Germany
• Proactive premature intention
estimation by determining the
• Earliest possible trigger state
in a Finite State Machine
representing the human
intention
• Selecting the most probable
intention prematurely for more
than one ambiguous human
intentions
• Selection of trigger state is based
on common state transition
sequence
• Premature intention recognition
by the weights of the transition
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Proactive premature intention
recognition. Top: earliest possible
trigger state selection for
proactive intention recognition.
Bottom: Premature intention
recognition
12:15–12:30 WedCT7.6
On-Line Human Action Recognition by Combining
Joint Tracking and Key Pose Recognition
E-Jui Weng and Li-Chen Fu
Department Name, University Name, Country
• Propose a boosting approach by
combining the pose estimation and the
upper body tracking to recognize human
actions.
• Our method can recognize human poses
and actions at the same time.
• Apply the action recognition results as a
feedback to the pose estimation process
to increase its efficiency and accuracy.
• Present an on-line spotting scheme based
on the gradients of the hidden Markov
models probabilities.
System overview