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Empirical Inference Talk 2010

Learning Motor Primitives for Robotics

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Empirical Inference
Jan Peters
Research Group Leader
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Empirical Inference
Jens Kober

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.

Author(s): Kober, J. and Peters, J.
Year: 2010
Month: January
Day: 15
Bibtex Type: Talk (talk)
Digital: 0
Electronic Archiving: grant_archive
Event Name: EVENT Lab: Reinforcement Learning in Robotics and Virtual Reality
Event Place: Barcelona, Spain
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex 

@talk{6254,
  title = {Learning Motor Primitives for Robotics},
  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.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jan,
  year = {2010},
  slug = {6254},
  author = {Kober, J. and Peters, J.},
  month_numeric = {1}
}