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 we address the challenging problem of sensorimotor integration, with reference to eye-hand coordination of an artificial agent engaged in a natural drawing task. Under the assumption that eye-hand coupling influences observed movements, a motor continuity hypothesis is exploited to account for how gaze shifts are constrained by hand movements. A Bayesian model of such coupling is presented in the form of a novel Dynamic Bayesian Network, namely an Input–Output Coupled Hidden Markov Model. Simulation results are compared to those obtained by eye-tracked human subjects involved in drawing experiments.

In this paper a neurosymbolic hybrid system for robot self-localization is proposed. This crucial issue in autonomous robotic research field has been tackled with slightly different approaches depending mainly on robot sensors and actuation devices, its available computational resources, and the environment in which it acts. The system we present has been designed for robot endowed with poor sensors (a 16-element sonar array, a camera just performing very light computation, odometric sensors) which acts in an office-like environment. In the proposed landmark based approach, we have considered as "natural" environmental features the corners formed by wall intersections, and corners formed by doors that are present in the scene. The whole problem, from landmark recognition to position estimation, is carried out by a unified neurosymbolic system.