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Learning passive motor control strategies with genetic algorithms


Book Chapter


This study investigates learning passive motor control strategies. Passive control is understood as control without active error correction; the movement is stabilized by particular properties of the controlling dynamics. We analyze the task of juggling a ball on a racket. An approximation to the optimal solution of the task is derived by means of optimization theory. In order to model the learning process, the problem is coded for a genetic algorithm in representations without sensory or with sensory information. For all representations the genetic algorithm is able to find passive control strategies, but learning speed and the quality of the outcome are significantly different. A comparison with data from human subjects shows that humans seem to apply yet different movement strategies to the ones proposed. For the feedback representation some implications arise for learning from demonstration.

Author(s): Schaal, S. and Sternad, D.
Book Title: 1992 Lectures in complex systems
Pages: 913-918
Year: 1993
Editors: Nadel, L.;Stein, D.
Publisher: Addison-Wesley

Department(s): Autonomous Motion
Bibtex Type: Book Chapter (inbook)

Address: Redwood City, CA
Cross Ref: p864
Note: clmc
URL: http://www-clmc.usc.edu/publications/S/schaal-SFI1993.pdf


  title = {Learning passive motor control strategies with genetic algorithms},
  author = {Schaal, S. and Sternad, D.},
  booktitle = {1992 Lectures in complex systems},
  pages = {913-918},
  editors = {Nadel, L.;Stein, D.},
  publisher = {Addison-Wesley},
  address = {Redwood City, CA},
  year = {1993},
  note = {clmc},
  crossref = {p864},
  url = {http://www-clmc.usc.edu/publications/S/schaal-SFI1993.pdf}