Learning VOR-like stabilization reflexes in robots.

Abstract

We present a binocular robot that learns compensatory camera movements for image stabilization purposes. Most essential in achieving satisfactory image stabilization performance is the exploitation/integration of different self-motion information. In our robot, self-motion is measured inertially through an artificial vestibular apparatus and visually using basic motion detection algorithms. The first sensory system codes rotations and translations of the robot's head, the second, the shift of the visual world across the image plane. An adaptive neural network learns to map these sensory signals to motor commands, transforming non homogeneous self-motion information into compensatory camera movements. We describe the network architecture, the convergence of the learning scheme and the performance of the stabilization reflex evaluated quantitatively by means of direct measurements on the image plane. 1. Introduction When moving through the environment it is of paramount imp...