TR-A-0086 :1990.7.23

Mitsuo KAWATO

Optimization and Learning in Neural Networks for Formation and Control of Coordinated Movement

Abstract:We proposed that the trajectory followed by human subject arms tended to minimize the time integral of the square of the rate of change of torque. Based on this computational model, the cascade neural network model, which utilizes a forward model of a controlled object, reproduced Fitts's law of speed-accuracy tradeoff as well as various invariant features of path and velocity profiles of multi-joint arm movement. For supervised motor learning, conversion of the error signal calculated in the task space into that of the motor command space is most essential and difficult. We proposed a feedback-error-learning approach in which the feedback motor command is used as an error signal to train an inverse model of the controlled object, which then generates a feedforward motor command. Here, we propose a unified neural network model which integrates the two previous models. In this model, for very skilled movements relaxation computation is conducted using both the forward and inverse models of the controlled object, while only the inverse model acquired by the feedback-error-learning is utilized for relatively difficult or less skilled movements.