Maturational constraints for motor learning in high-dimensions: The case of biped walking

Abstract

This paper outlines a new developmental approach to motor learning in very high-dimensions, applied to learning biped locomotion in humanoid robots. This approach relies on the formal modeling and coupling of several advanced mechanisms inspired from human development for actively controlling the growth of complexity and harnessing the curse of dimensionality: 1) Maturational constraints for the progressive release of new degrees of freedoms and progressive increase their explorable ranges; 2) Motor synergies; 3) Morphological computation; 4) Social Guidance. An experimental setup involving a simulated version of the Acroban Humanoid robot is presented.