Optimal unsupervised motor learning for dimensionality reduction of nonlinear control systems

Abstract

In this paper, optimal unsupervised motor learning is defined to be a technique for finding the coordinate system of minimum dimensionality which can adequately describe a particular motor task. An explicit method is provided for learning a stable controller that translates commands within the new coordinate system into motor variables appropriate for plant control. The method makes use of previously described neural network algorithms including the generalized Hebbian algorithm, basis-function trees, and trajectory extension learning. Examples of applications to a real direct-drive two joint planar robot arm and a simulated three joint robot arm with visual sensing are given.