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  Reinforcement Learning to adjust Robot Movements to New Situations

Kober, J., Oztop, E., & Peters, J. (2011). Reinforcement Learning to adjust Robot Movements to New Situations. Robotics: Science and Systems VI, 33-40.

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 Creators:
Kober, J1, 2, Author              
Oztop, E, Author
Peters, J1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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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.

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 Dates: 2011-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 6438
DOI: 10.15607/RSS.2010.VI.005
 Degree: -

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Title: 2010 Robotics: Science and Systems Conference (RSS 2010)
Place of Event: Zaragoza, Spain
Start-/End Date: 2011-06-27 - 2011-06-30

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Title: Robotics: Science and Systems VI
Source Genre: Journal
 Creator(s):
Matsuoka, Y, Editor
Durrant-Whyte, HF, Editor
Neira, J, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 33 - 40 Identifier: ISBN: 978-0-262-51681-5