Visuo-Motor Remapping for 3D, 6D and Tool-Use Reach using Gain-Field Networks

Abstract

Reaching and grasping objects in 3D is still a challenging task in robotics because they have to be done in an integrated fashion, as it is for tool-use or during imitation with a human partner. The visuo-motor networks in the human brain exploit a neural mechanism known as gain-field modulation to adapt different circuits together with respect to the task and for parsimony purpose. In this paper, we show how gain-field neural networks achieve the learning of visuo-motor cells sensitive to the 3D direction of the arm motion (3D reaching), to the 3D reaching + 3D orientation of the hand (6D reaching) and to the 3D direction of tool tip (tool-use reaching) when this new information is added to the network. Experiments on robotic simulations demonstrate the accuracy of control and the efficient remapping to the new coordinate system.