Luca Bascetta

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In the last decade, policy gradient methods have significantly grown in popularity in the reinforcement–learning field. In particular, they have been largely employed in motor control and robotic applications, thanks to their ability to cope with continuous state and action domains and partial observable problems. Policy gradient researches have been mainly(More)
— Planning and control for a wheeled mobile robot are challenging problems when poorly traversable terrains, including dynamic obstacles, are considered. To accomplish a mission, the control system should firstly guarantee the vehicle integrity, for example with respect to possible roll-over/tip-over phenomena. A fundamental contribution to achieve this(More)
— This work investigates the way humans plan their paths in a goal-directed motion. The person can be viewed as an optimal controller that plans the path minimizing a certain (unknown) cost function. Taking this viewpoint, the problem can be formulated as an inverse optimal control one, i.e., starting from control and state trajectories we want to figure(More)