Learning an internal representation of the end-effector configuration space

Abstract-- Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or end-effector position. This paper proposes a method to estimate a certain aspect of the forward kinematics model with no such information. An internal representation of the end-effector configuration is generated from unstructured proprioceptive and exteroceptive data flow under very limited assumptions. A mapping from the proprioceptive space to this representational space can then be used to control the robot. I. INTRODUCTION One of the problems an autonomous robot must be able to solve is to retrieve basic information about its own topological structure relying on minimal a priori information.