Nonlinear force fields: A distributed system of control primitives for representing and learning movements

F. A. Mussa-Ivaldi*

*Corresponding author for this work

Research output: Contribution to conferencePaper

51 Scopus citations

Abstract

Electrophysiological studies have suggested the presence of a modular structure in the output stages of the motor system. In this structure, independent modules are connected to specific groups of muscles and generate nonlinear fields of force acting upon the controlled limbs. This paper explores the computational consequences of this structure in the framework of multivariate approximation. Movements are generated through the selection of independent modules and through the vectorial superposition of their output fields. It is shown that complex joint motions of a multi-segmental mechanism may be obtained by determining a set of time-independent parameters which scale the amplitude of each module's field. In addition, optimization results suggest that a system of such modules may evolve to improve the execution of smooth movements of the mechanism's endpoint across the whole workspace. The observed improvements generalize beyond the set of movements used to guide the optimization. These findings indicate that a rich repertoire of behaviors may be learned by adapting a system of force fields obtained from the combination of multiple viscoelastic actuators.

Original languageEnglish (US)
Pages84-90
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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    Mussa-Ivaldi, F. A. (1997). Nonlinear force fields: A distributed system of control primitives for representing and learning movements. 84-90. Paper presented at Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA, Monterey, CA, USA, .