Robotics: Science and Systems VI

Reinforcement Learning to adjust Robot Movements to New Situations

J. Kober, E. Oztop and J. Peters

Abstract:

Many complex robot motor skills can be represented using elementary movements, and there exist efficient techniques for learning parametrized motor plans using demonstrations and self-improvement. However, in many cases, the robot currently needs to learn a new elementary movement even if a parametrized motor plan exists that covers a similar, related situation. Clearly, a method is needed that modulates the elementary movement through the meta-parameters of its representation. In this paper, we show how to learn such mappings from circumstances to meta-parameters using reinforcement learning. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. We compare this algorithm to several previous methods on a toy example and show that it performs well in comparison to standard algorithms. Subsequently, we show two robot applications of the presented setup; i.e., the generalization of throwing movements in darts, and of hitting movements in table tennis. We show that both tasks can be learned successfully using simulated and real robots.

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Bibtex:

@INPROCEEDINGS{ Kober-RSS-10,
    AUTHOR    = {J. Kober AND E. Oztop AND J. Peters},
    TITLE     = {Reinforcement Learning to adjust Robot Movements to New Situations},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems},
    YEAR      = {2010},
    ADDRESS   = {Zaragoza, Spain},
    MONTH     = {June},
    DOI       = {10.15607/RSS.2010.VI.005} 
}