A Self-Organizing Neural Model for Fault-Tolerant Control of Redundant Robots

Abstract

This paper describes a self-organizing neural model that is capable of controlling the kinematics of robots with redundant degrees of freedom. The self-organized learning process is based on action perception cycles where the robot is perturbed minimally about a given joint configuration and learns to map these perturbations to changes in sensor readings corresponding to these minimal perturbations. This motor babbling phase provides self-generated movement commands that activate correlated sensory, spatial and motor information that are used to learn an internal coordinate transformation between sensory and motor systems. This idea was tested on two different tasks: reaching targets in 2-D space with a three degree of freedom robot and saccading to targets in 3-D with a twelve degree of freedom head-neck-eye system. Computer simulations show that the resulting controller is highly fault-tolerant and robust to previously unseen disturbances much like biological systems.