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  Robot Skill Learning

Peters, J., Mülling, K., Kober, J., Nguyen-Tuong, D., & Krömer, O. (2012). Robot Skill Learning. In L. De Raedt, C. Bessiere, D. Dubois, P. Doherty, P. Frasconi, F. Heintz, et al. (Eds.), 20th European Conference on Artificial Intelligence (ECAI 2012) (pp. 40-45). Amsterdam, Netherlands: IOS Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B68E-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-1BDF-7
Genre: Conference Paper

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http://ebooks.iospress.nl/publication/6945 (Publisher version)
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 Creators:
Peters, J1, Author              
Mülling, K1, Author              
Kober, J1, Author              
Nguyen-Tuong, D1, Author              
Krömer, O1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: Learning robots that can acquire new motor skills and refine existing ones have 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 be adequate for robot 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 the domain. As an alternative, we propose to divide the generic skill learning problem into parts that can be well-understood 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 robot skill learning. In this paper, we discuss our recent and current progress in this direction. As such, we present our work on learning to control, learning elementary movements, as well as our steps towards the learning of complex tasks. We show several evaluations using both real robots as well as physically realistic simulations.

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 Dates: 2012-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.3233/978-1-61499-098-7-40
BibTex Citekey: PetersMKNK2012
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Title: 20th European Conference on Artificial Intelligence (ECAI 2012)
Place of Event: Montpellier, France
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Source 1

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Title: 20th European Conference on Artificial Intelligence (ECAI 2012)
Source Genre: Proceedings
 Creator(s):
De Raedt, L, Editor
Bessiere, C, Editor
Dubois, D, Editor
Doherty, P, Editor
Frasconi, P, Editor
Heintz, F, Editor
Lucas, P, Editor
Affiliations:
-
Publ. Info: Amsterdam, Netherlands : IOS Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 40 - 45 Identifier: ISBN: 978-1-61499-097-0

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Title: Frontiers in Artificial Intelligence and Applications ; 242
Source Genre: Series
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -