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Conference Paper

Reinforcement Learning to adjust Robot Movements to New Situations

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Kober,  J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Peters,  J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Kober, J., Oztop, E., & Peters, J. (2011). Reinforcement Learning to adjust Robot Movements to New Situations. In Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011) (pp. 2650-2655).


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-4CA9-D
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 with current techniques, in many cases, the robot currently needs to learn a new elementary movement even if a parametrized motor plan exists that covers a related situation. A method is needed that modulates the elementary movement through the meta-parameters of its representation. In this paper, we describe how to learn such mappings from circumstances to meta-parameters using reinforcement learning. In particular we use a kernelized version of the reward-weighted regression. We show two robot applications of the presented setup in robotic domains; the generalization of throwing movements in darts, and of hitting movements in table tennis. We demonstrate that both tasks can be learned successfully using simulated and real robots.