Computational elements of robot learning by imitation

Robot learning by imitation makes an increasing body of robotics research. Imitation learning complements motor learning techniques by restricting the search space to a computationally tractable subset. Imitation learning search for spatial and temporal invariants across several demonstrations. These invariants are task- dependent. This talk will present an algorithm that determines the key features of an imitation task through a comparative analysis of the data in joint space, carthesian space and visual space. These features are used to control the reproduction of the observed motion by a 30 degrees of freedom humanoid robot.

Published in:
Proceedings of the 980th American Mathematical Society, 980
Presented at:
980th American Mathematical Society, University of Wisconsin, Madison, Wisconsin, Oct. 12-13

 Record created 2005-11-16, last modified 2018-07-07

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