Failure Modes in Feedback Error Learning

This paper examines the feedback error learning architecture that has been proposed by Kawato under specific conditions in which learning does not occur. When a robot attempts to learn a novel task in an unknown environment, an approximation of the inverse dynamics of the plant/environment may be required. The novelty of feedback error learning lies in the training signal used to iteratively construct the inverse feedforward controller to achieve this task. We discuss a class of failure modes where the interaction of the learning algorithm and the feedback control leads to poor performance despite repeated practice. We hypothesize that this model could describe motor learning failure commonly seen in childhood movement disorders where a task, such as reaching in a straight path to an intended target, is never learned or improved despite years of repeated practice.