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Learning new basic Movements for Robotics


Conference Paper


Obtaining novel skills is one of the most important problems in robotics. Machine learning techniques may be a promising approach for automatic and autonomous acquisition of movement policies. However, this requires both an appropriate policy representation and suitable learning algorithms. Employing the most recent form of the dynamical systems motor primitives originally introduced by Ijspeert et al. [1], we show how both discrete and rhythmic tasks can be learned using a concerted approach of both imitation and reinforcement learning, and present our current best performing learning algorithms. Finally, we show that it is possible to include a start-up phase in rhythmic primitives. We apply our approach to two elementary movements, i.e., Ball-in-a-Cup and Ball-Paddling, which can be learned on a real Barrett WAM robot arm at a pace similar to human learning.

Author(s): Kober, J. and Peters, J.
Book Title: AMS 2009
Journal: Autonome Mobile Systeme 2009: 21. Fachgespr{\"a}ch
Pages: 105-112
Year: 2009
Month: December
Day: 0
Editors: Dillmann, R. , J. Beyerer, C. Stiller, M. Z{\"o}llner, T. Gindele
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/978-3-642-10284-4_14
Event Name: Autonome Mobile Systeme 2009
Event Place: Karlsruhe, Germany

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Learning new basic Movements for Robotics},
  author = {Kober, J. and Peters, J.},
  journal = {Autonome Mobile Systeme 2009: 21. Fachgespr{\"a}ch},
  booktitle = {AMS 2009},
  pages = {105-112},
  editors = {Dillmann, R. , J. Beyerer, C. Stiller, M. Z{\"o}llner, T. Gindele},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = dec,
  year = {2009},
  month_numeric = {12}