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Towards Machine Learning of Motor Skills


Conference Paper



Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks that a robot should fulfill. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. 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, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach to motor skill learning in order to get one step closer towards human-like performance. For doing so, we study two ma jor components for such an approach, i.e., firstly, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting.

Author(s): Peters, J. and Schaal, S. and Schölkopf, B.
Book Title: Proceedings of Autonome Mobile Systeme (AMS)
Pages: 138-144
Year: 2007
Editors: K Berns and T Luksch

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

DOI: 10.1007/978-3-540-74764-2_22

Cross Ref: p10121
Note: clmc

Links: PDF


  title = {Towards Machine Learning of Motor Skills},
  author = {Peters, J. and Schaal, S. and Sch{\"o}lkopf, B.},
  booktitle = {Proceedings of Autonome Mobile Systeme (AMS)},
  pages = {138-144},
  editors = {K Berns and T Luksch},
  year = {2007},
  note = {clmc},
  crossref = {p10121}