Learning Sensory-Motor Maps for Redundant Robots

Abstract

Humanoid robots are routinely engaged in tasks requiring the coordination between multiple degrees of freedom and sensory inputs, often achieved through the use of sensory-motor maps (SMMs). Most of the times, humanoid robots have more degrees of freedom (DOFs) available than those necessary to solve specific tasks. Notwithstanding, the majority of approaches for learning these SMMs do not take that into account. At most, the redundant degrees of freedom (degrees of redundancy, DOR) are "frozen" with some auxiliary criteria or heuristic rule. We present a solution to the problem of learning the forward/backward model, when the map is not injective, as in redundant robots. We propose the use of a "minimum order SMM" that takes the desired image configuration and the DORs as input variables, while the non-redundant DOFs are viewed as outputs. Since the DORs are not frozen in this process, they can be used to solve additional tasks or criteria. This method provides a global solution for positioning a robot in the workspace, without the need to move in an incremental way. We provide examples where these tasks correspond to optimization criteria that can be solved online. We show how to learn the "minimum order SMM" using a local statistical learning method. Extensive experimental results with a humanoid robot are discussed to validate the approach, showing how to learn the minimum order SMM of a redundant system and using the redundancy to accomplish auxiliary tasks