Visual Learning for Reaching and Body-Schema with Gain-Field Networks

Abstract

Perceiving our own body posture improves the way we move dynamically and reversely, motion coordination serves to learn better the position of our own body. Following this idea, we present a neural architecture toward reaching movements and body self-perception from a developmental perspective. Our framework is based on the neurobiological mechanism known as gain modulation in parietal neurons that is found to integrate the visual, motor and proprioceptive information through product-like processes. These multiplicative networks have interesting properties for learning nonlinear transformations such as the head-centered mapping in reaching tasks or the hand-centered mapping for a body-centered representation. In a simulation of a three-link arm, we perform experiments of nearby and far reach targets exploiting one or the other strategy. The later combination of the two networks generates autonomous control toward the target by processing the body-centered spatial information and the preferred visual direction for the desired motor commands.