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  Towards Motor Skill Learning for Robotics

Peters, J., Mülling, K., Kober, J., Nguyen-Tuong, D., & Kroemer, O. (2011). Towards Motor Skill Learning for Robotics. In C. Pradalier, R. Siegwart, & G. Hirzinger (Eds.), Robotics Research: The 14th International Symposium ISRR (pp. 469-482). Berlin, Germany: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BCE2-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0001-ACCB-9
Genre: Conference Paper

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https://link.springer.com/content/pdf/10.1007%2F978-3-642-19457-3_28.pdf (Publisher version)
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 Creators:
Peters, J1, 2, Author           
Mülling, K1, 2, Author           
Kober, J1, 2, Author           
Nguyen-Tuong, D1, 2, Author           
Kroemer, O1, 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: Learning robots that can acquire new motor skills and refine existing one has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early steps towards this goal in the 1980s made clear that reasoning and human insights will not suffice. Instead, new hope has been offered by the rise of modern machine learning approaches. However, to date, it becomes increasingly clear that off-the-shelf machine learning approaches will not suffice for motor skill learning as these methods often do not scale into the high-dimensional domains of manipulator and humanoid robotics nor do they fulfill the real-time requirement of our domain. As an alternative, we propose to break the generic skill learning problem into parts that we can understand well from a robotics point of view. After designing appropriate learning approaches for these basic components, these will serve as the ingredients of a general approach to motor skill learning. In this paper, we discuss our recent and current progress in this direction. For doing so, we present our work on learning to control, on learning elementary movements as well as our steps towards learning of complex tasks. We show several evaluations both using real robots as well as physically realistic simulations.

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 Dates: 2011-01
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-19457-3_28
BibTex Citekey: 6069
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Title: 14th International Symposium on Robotics Research (ISRR 2009)
Place of Event: Luzern, Switzerland
Start-/End Date: 2009-08-31 - 2009-09-03

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Title: Robotics Research: The 14th International Symposium ISRR
Source Genre: Proceedings
 Creator(s):
Pradalier, C, Editor
Siegwart, R, Editor
Hirzinger, G, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 469 - 482 Identifier: ISBN: 978-3-642-19456-6

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Title: Springer Tracts in Advanced Robotics
Source Genre: Series
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Pages: - Volume / Issue: 70 Sequence Number: - Start / End Page: - Identifier: -