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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 420038.0, MPI für biologische Kybernetik / Biologische Kybernetik
Policy Learning for Motor Skills
Authors:Peters, J.; Schaal, S.
Editors:Ishikawa, M.; Doya, K.; Miyamoto, H.; Yamakawa, T.
Date of Publication (YYYY-MM-DD):2008-06
Title of Proceedings:Neural Information Processing: 14th International Conference ICONIP 2007
Start Page:233
End Page:242
Physical Description:10
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Policy learning which allows autonomous robots to adapt to novel situations has 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, and usually scaling was 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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI f�r biologische Kybernetik/Empirical Inference (Dept. Sch�lkopf)
Identifiers:LOCALID:4869
URL:http://conf.lsse.kyutech.ac.jp/~iconip2007/iconip2...
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