AUTONOMOUS ONLINE LEARNING OF REACHING BEHAVIOR IN A HUMANOID ROBOT

Abstract

In this paper we describe an autonomous strategy which enables a humanoid robot to learn how to reach for a visually identified object in the 3D space. The robot is a 22-DOF upper-body humanoid with moving eyes, neck, arm and hand. The robot is bootstrapped with limited a-priori knowledge, sufficient to start the interaction with the environment; this interaction allows the robot to learn different sensorimotor mappings, required for reaching. The arm-head forward kinematic model and a visuo-motor inverse model are learned from sensory experience. Learning is performed purely online (without any separation between training and execution) through a goal-directed exploration of the environment. During the learning the robot is also able to build an internal representation of its reachable space.