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Algorithmen zum Automatischen Erlernen von Motorfähigkeiten

2010

Article

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Robot learning methods which allow autonomous 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 fulfill 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 approach 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 algorithms 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 structures for task representation and execution.

Author(s): Peters, J. and Kober, J. and Schaal, S.
Journal: at - Automatisierungstechnik
Volume: 58
Number (issue): 12
Pages: 688-694
Year: 2010
Month: December
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1524/auto.2010.0880
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@article{6804,
  title = {Algorithmen zum Automatischen Erlernen von Motorf{\"a}higkeiten},
  author = {Peters, J. and Kober, J. and Schaal, S.},
  journal = {at - Automatisierungstechnik},
  volume = {58},
  number = {12},
  pages = {688-694},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2010},
  doi = {10.1524/auto.2010.0880},
  month_numeric = {12}
}