Sensorimotor coupling via Dynamic Bayesian Networks

Abstract — In this paper we consider the problem of sensori-motor coordination in a Bayesian framework. To this end we introduce a novel kind of Dynamic Bayesian Network serving as the core tool to integrate active vision and task-constrained motor behaviors. The proposed system is put into work by addressing the challenging task of realistic drawing performed by a robotic agent, namely a 7-DOF anthropomorphic manip-ulator. Simulation results are compared to those obtained by eye-tracked human subjects involved in drawing experiments. I