Perception-Driven Neural-Based Potentials for Mobile Robot Control

In this work, we investigate how to leverage learning processes to design a perception-driven control framework for robot motion. In this regard, inspired by the fact that potential-based control represents an effective approach for modeling robotic tasks, we study how neural networks can be effectively exploited to approximate unknown perception-based potential functions, for which an analytical closed form may not even be available, thus extending the field of applicability of potential-based control. Numerical results along with an experimental validation are provided to empirically demonstrate the validity of the proposed control architecture.