Reinforcement Learning to adjust Robot Movements to New Situations
Jens Kober, Erhan Öztop, Jan Peters · 2010
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.