An integrated motor control loop of a human-like robotic arm: feedforward, feedback and cerebellum-based learning

Abstract

A new complex model of human motor control has been developed, combining brain internal models and neural network mechanisms. Based on nervous system structures and operating principles, a feedforward block, a feedback controller and a cerebellum-like learning module have been integrated and tested with an anthropometric robotic arm. A simulated sequence of 8-like tracking tasks showed the contributions of these main loops over time. Different external dynamics were introduced. The role of feedback corrections, intrinsically imprecise due to sensorimotor delays, decreases, while the output of cerebellum, which has been learning, increases; the movement becomes more accurate. Moreover, an experimental session on a subject performing the task repetitions using a haptic device was carried out, recording upper limb kinematics.