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Policy learning algorithmis for motor learning (Algorithmen zum automatischen Erlernen von Motorfähigkigkeiten)




Robot learning methods which allow au- tonomous robots to adapt to novel situations have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to ful- fill 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. If possible, scaling was usually only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general ap- proach 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 algo- rithms 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 structu- res for task representation and execution.

Author(s): Peters, J. and Kober, J. and Schaal, S.
Book Title: Automatisierungstechnik
Volume: 58
Number (issue): 12
Pages: 688-694
Year: 2010

Department(s): Autonomous Motion
Bibtex Type: Article (article)

Cross Ref: p10417
Note: clmc
URL: http://www-clmc.usc.edu/publications/P/peters-Auto2010.pdf


  title = {Policy learning algorithmis for motor learning (Algorithmen zum automatischen Erlernen von Motorfähigkigkeiten)},
  author = {Peters, J. and Kober, J. and Schaal, S.},
  booktitle = {Automatisierungstechnik},
  volume = {58},
  number = {12},
  pages = {688-694},
  year = {2010},
  note = {clmc},
  crossref = {p10417},
  url = {http://www-clmc.usc.edu/publications/P/peters-Auto2010.pdf}