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Learning Motor Primitives for Robotics

2009

Talk

ei


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. and Oztop, E.
Year: 2009
Month: June
Day: 11

Department(s): Empirical Inference
Bibtex Type: Talk (talk)

Digital: 0
Event Name: Advanced Telecommunications Research Center ATR
Event Place: Kyoto, Japan
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex

@talk{6255,
  title = {Learning Motor Primitives for Robotics},
  author = {Kober, J. and Peters, J. and Oztop, E.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2009},
  month_numeric = {6}
}