Information management for gaze control in vision guided biped walking

Abstract

This article deals with the information management for active gaze control in the context of vision-guided humanoid walking. The proposed biologically inspired predictive gaze control strategy is based on the maximization of visual information. The quantification of the information requires a stochastic model of both, the robot and perception system. The information/uncertainty management, i.e. relationships between the system, state estimation and the active measurements, employs a coupled (considering cross-covariances) hybrid (reflecting the discontinuous character of biped walking) extended (copes with nonlinear systems) Kalman filter approach.