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Learning-based
physical human robot interaction July 30,
2012 11:00AM 302- 309-1 Abstract: In this talk,
research activities of physical human robot interaction in Dynamic Human
Robot Interaction lab at TUM will be presented. In this context of autonomous
robotic learning, unsupervised incremental learning techniques for
segmentation and clustering are applied to enhance human-robot cooperation
tasks over time with the special focus on prediction of human partner’s
behavior. The learned skills can be further improved by kinesthetic coaching,
which leads to eliminating kinematic mapping errors and learning synchronized
whole body motions. Finally the extension towards learning physical human
robot interaction, where a robot companion is capable of handling intentional
physical contacts with a human user, will be discussed. Direct physical
interaction with a human during task execution is a widely undiscovered
challenge. In our method, communication is designed in both symbolic and
physical domains. The communication in the symbolic domain is realized
through the concept of motion primitives and interaction primitives. In the
physical domain, the trajectory of the motion primitive is reshaped in
accordance with the human in real-time. This page is
maintained by Yumi Yi (ymyi@bi.snu.ac.kr).
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