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Reinforcement Learning to adjust Robot Movements to New Situations

2011

Conference Paper

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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.

Author(s): Kober, J. and Oztop, E. and Peters, J.
Book Title: Robotics: Science and Systems VI
Journal: Robotics: Science and Systems VI
Pages: 33-40
Year: 2011
Month: September
Day: 0
Editors: Matsuoka, Y. , H. F. Durrant-Whyte, J. Neira
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 2010 Robotics: Science and Systems Conference (RSS 2010)
Event Place: Zaragoza, Spain

Address: Cambridge, MA, USA
Digital: 0
ISBN: 978-0-262-51681-5
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{6438,
  title = {Reinforcement Learning to adjust Robot Movements to New Situations},
  author = {Kober, J. and Oztop, E. and Peters, J.},
  journal = {Robotics: Science and Systems VI},
  booktitle = {Robotics: Science and Systems VI},
  pages = {33-40},
  editors = {Matsuoka, Y. , H. F. Durrant-Whyte, J. Neira},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = sep,
  year = {2011},
  month_numeric = {9}
}