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  Algorithmen zum Automatischen Erlernen von Motorfähigkeiten

Peters, J., Kober, J., & Schaal, S. (2010). Algorithmen zum Automatischen Erlernen von Motorfähigkeiten. At-Automatisierungstechnik, 58(12), 688-694. doi:10.1524/auto.2010.0880.

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 Creators:
Peters, J1, 2, Author           
Kober, J1, 2, Author           
Schaal, S, 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: Robot learning methods which allow autonomous robots to adapt to novel situations have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics. If possible, scaling was usually only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i. e., firstly, we study policy learning algorithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structures for task representation and execution.

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 Dates: 2010-12
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1524/auto.2010.0880
BibTex Citekey: 6804
 Degree: -

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Title: At-Automatisierungstechnik
Source Genre: Journal
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Publ. Info: München : Oldenbourg
Pages: - Volume / Issue: 58 (12) Sequence Number: - Start / End Page: 688 - 694 Identifier: ISSN: 0178-2312
CoNE: https://pure.mpg.de/cone/journals/resource/958480278103