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IJCAI
2007

Learning to Walk through Imitation

8 years 7 months ago
Learning to Walk through Imitation
Programming a humanoid robot to walk is a challenging problem in robotics. Traditional approaches rely heavily on prior knowledge of the robot's physical parameters to devise sophisticated control algorithms for generating a stable gait. In this paper, we provide, to our knowledge, the first demonstration that a humanoid robot can learn to walk directly by imitating a human gait obtained from motion capture (mocap) data. Training using human motion capture is an intuitive and flexible approach to programming a robot but direct usage of mocap data usually results in dynamically unstable motion. Furthermore, optimization using mocap data in the humanoid full-body joint-space is typically intractable. We propose a new modelfree approach to tractable imitation-based learning in humanoids. We represent kinematic information from human motion capture in a low dimensional subspace and map motor commands in this lowdimensional space to sensory feedback to learn a predictive dynamic model...
Rawichote Chalodhorn, David B. Grimes, Keith Groch
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Rawichote Chalodhorn, David B. Grimes, Keith Grochow, Rajesh P. N. Rao
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