Adaptive saccade control of a Binocular Head with Dynamic Cell Structures

Abstract

In this article we report how Dynamic Cell Structures (DCS) [1] can be utilized to learn fast and accurate saccade control of a four-degrees-of-freedom Binocular Head. We solve the order selection problem by incremental growing of a DCS network until the controller meets a pre-specified precision. Calculation of the controller output is very fast and suitable for realtime control since the resulting network is as small as possible and only the best matching unit and its topological neighbors are activated on presentation of an input stimulus. Training of the DCS is based on error feedback learning and proceeds in two phases. In the first phase we use a crude model of the cameras and the kinematics of the head to learn the topology of the input submanifold and a rough approximation off-line. In a second phase, the operating phase, we employ error feedback learning for online adaptation of the linear output units. Besides our TRC binocular head we use a Datacube image processing system and a Stäubli R90 robot arm for automated training in the second phase. The controller is demonstrated to successfully correct errors in the model and to rapidly adapt to changing parameters.KeywordsInverse KinematicError FeedbackController OutputNeural UnitVergence AngleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.