@inproceedings{KoberOP2011,
  title = {Reinforcement Learning to adjust Robot Movements to New Situations},
  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.},
  pages = {2650-2655},
  editors = {Walsh, T.},
  publisher = {AAAI Press},
  address = {Menlo Park, CA, USA},
  month = jul,
  year = {2011},
  slug = {koberop2011},
  author = {Kober, J. and Oztop, E. and Peters, J.},
  month_numeric = {7}
}