Learning Internal Models for Eye-Hand Coordination in Reaching and Grasping

A computational model of sensorimotor transformations underlying eye-hand coordination in reaching and grasping is presented and tested in a robot-arm setup. We suggest solutions for both the problem of the missing teacher signal and for the problem of one-to-many mappings encountered when learning inverse models and controllers.