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  Learning Motor Primitives for Robotics

Kober, J., Peters, J., & Oztop, E. (2009). Learning Motor Primitives for Robotics. Talk presented at Advanced Telecommunications Research Center ATR. Kyoto, Japan. 2009-06-11.

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 Creators:
Kober, J1, 2, Author           
Peters, J1, 2, Author           
Oztop, E, 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: The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.

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 Dates: 2009-06
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 6255
 Degree: -

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Title: Advanced Telecommunications Research Center ATR
Place of Event: Kyoto, Japan
Start-/End Date: 2009-06-11
Invited: Yes

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