Evaluation of a probabilistic approach to learn and reproduce gestures by imitation

We present an approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) to learning robust models of human motion through imitation. The proposed approach allows us to extract redundancies across multiple demonstrations and build time-independent models to reproduce the dynam...

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Bibliographic Details
Main Authors: Calinon, Sylvain, Sauser, Eric L, Billard, Aude G, Caldwell, Darwin G
Format: Conference Proceeding
Language:English
Subjects:
Adaptive control
Encoding
Hidden Markov models
Humans
Programmable control
Robot programming
Robotics and automation
Robustness
Spline
USA Councils
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Summary:We present an approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) to learning robust models of human motion through imitation. The proposed approach allows us to extract redundancies across multiple demonstrations and build time-independent models to reproduce the dynamics of the demonstrated movements. The approach is systematically evaluated by using automatically generated trajectories sharing similarities with human gestures. The proposed approach is contrasted with four state-of-the-art methods previously proposed in robotics to learn and reproduce new skills by imitation. An experiment with a 7 DOFs robotic arm learning and reproducing the motion of hitting a ball with a table tennis racket is then presented to illustrate the approach.
ISSN:1050-4729
2577-087X
DOI:10.1109/ROBOT.2010.5509988