A Bio-inspired Neural Sensory-Motor Coordination Scheme for Robot Reaching and Preshaping

Abstract

We present a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach for and to grasp an object, while pre-shaping the fingers to the required grasp configuration. A model for sensory-motor coordination derived from studies in humans inspired the development of the scheme. A special feature of this model is the prediction of the tactile image perceived after grasping. The proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for grasping, selects the best-suited hand configuration, and predicts the tactile feedback after grasping. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control