Reinforcement Learning to Adjust Robot Movements to New Situations

Kober, Jens; Öztop, Erhan; Peters, Jan

doi:10.5591/978-1-57735-516-8/IJCAI11-441

Reinforcement Learning to Adjust Robot Movements to New Situations

Jens Kober, Erhan Öztop, Jan Peters

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 2650-2654.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-441

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.

Jens Kober, Erhan Öztop, and Jan Peters. 2011. Reinforcement Learning to Adjust Robot Movements to New Situations. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 2650–2654. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-441

@inproceedings{ijcai2011p441,
  title     = {Reinforcement Learning to Adjust Robot Movements to New Situations},
  author    = {Kober, Jens and Öztop, Erhan and Peters, Jan},
  booktitle = {Proceedings of the Twenty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-11}},
  publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
  editor    = {Toby Walsh},
  pages     = {2650--2654},
  year      = {2011},
  month     = {7},
  doi       = {10.5591/978-1-57735-516-8/IJCAI11-441},
  url       = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-441},
}

TY  - CPAPER
TI  - Reinforcement Learning to Adjust Robot Movements to New Situations
AU  - Kober, Jens
AU  - Öztop, Erhan
AU  - Peters, Jan
T2  - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11
PB  - AAAI Press/International Joint Conferences on Artificial Intelligence
PY  - 2011
SP  - 2650
EP  - 2654
DO  - 10.5591/978-1-57735-516-8/IJCAI11-441
UR  - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-441
ER  -