A computational framework for integrating robotic exploration and human demonstration in imitation learning

Abstract

This paper proposes a computational framework for humanoid robots to learn complex behaviors through combining robotic self-exploration and demonstrations of humans. A modified Rapidly-growing Random Tree (RRT)-Connect algorithm is used for exploration, a Linear Global Model (LGM) is used for recording demonstrations, a spatial-temporal extension of Isomap… (More)
DOI: 10.1109/ICSMC.2011.6084053

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Cite this paper

@article{Tan2011ACF, title={A computational framework for integrating robotic exploration and human demonstration in imitation learning}, author={Huan Tan and Kazuhiko Kawamura}, journal={2011 IEEE International Conference on Systems, Man, and Cybernetics}, year={2011}, pages={2501-2506} }