In this paper we consider the problem of sensorimotor 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 manipulator. Simulation results are compared to those obtained by eye-tracked human subjects involved in drawing experiments.

In this paper the problem of automatic trajectory planning and inverse kinematics for a robotic system purposely designed to extinguish fires in road and railway tunnels is considered. The robot is composed by a self-cooling monorail vehicle carrying a fire fighting monitor. A fuzzy inference system is adopted for the automatic generation of the task-space trajectory for the robot and to solve the inverse kinematics problem in the presence of redundant degrees of freedom. Redundancy also allows assigning additional tasks besides the primary task. Simulation case studies are presented to test the performance of the whole system in a typical intervention scenario.