Implementation of an adaptive neural controller for sensory-motor coordination

Abstract

A theory and prototype of a neural controller called INFANT, which learns sensory-motor coordination from its own experience, is presented. INFANT adapts to unforeseen changes in the geometry of the physical motor system and to the location, orientation, shape, and size of objects. It can learn to accurately grasp an elongated object without any information about the geometry of the physical sensory-motor system. INFANT relies on the self-consistency between sensory and motor signals to achieve unsupervised learning. It is designed to be generalized for coordinating any number of sensory inputs with limbs of any number of joints. INFANT is implemented with an image processor, stereo cameras, and a five-degree-of-freedom robot arm. After learning, its average position accuracy is within 3% of the length of the arm, and its orientation accuracy is within 60 degrees in solid angle.