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Reinforcement Learning to Adjust Robot Movements to New Situations

Jens Kober; Erhan Öztop; Jan Peters
In: Toby Walsh (Hrsg.). IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence (IJCAI-2011), July 16-22, Barcelona, Spain, Pages 2650-2655, IJCAI/AAAI, 2011.


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; ie, 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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