F. A. Mussa-Ivaldi
Northwestern University Medical School
Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 84-90, 1997. (Winner of the Best Paper Award)
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.
Mussa-Ivaldi, F. A. (1997). “Nonlinear force fields: a distributed system of control primitives for representing and learning movements.” Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, 84-90