Model of cerebellar learning for control of arm movements using muscle synergies

Andrew H. Fagg*, Nathan Sitkoff, Andrew G. Barto, James C. Houk

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Biological control systems have long been studied as possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. In this paper, we present a model of cerebellar control of a muscle-actuated, two-link, planar arm. The model learns in a trial-and-error fashion to generate the appropriate sequence of motor signals that accurately bring the arm to a specified target. The motor signals produced by the cerebellum are specified in muscle synergy space. When the cerebellum fails to bring the arm to the target, an extra-cerebellar module performs low-quality connective movements, from which the cerebellum updates its program. In learning to perform the task, the cerebellum constructs an implicit inverse model of the plant. This model uses a combination of delayed sensory signals and recently-generated motor commands to compute the new output motor signal.

Original languageEnglish (US)
Title of host publicationProceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
PublisherIEEE
Pages6-12
Number of pages7
StatePublished - Jan 1 1997
EventProceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA - Monterey, CA, USA
Duration: Jul 10 1997Jul 11 1997

Other

OtherProceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
CityMonterey, CA, USA
Period7/10/977/11/97

ASJC Scopus subject areas

  • Computational Mathematics

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