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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.
