Francesco De Lellis;Marco Coraggio;Nathan C. Foster;Riccardo Villa;Cristina Becchio;Mario Di Bernardo
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Data-Driven Architecture to Encode Information in the Kinematics of Robots and Artificial Avatars
We present a data-driven control architecture designed to encode specific information, such as the presence or absence of an emotion, in the movements of an avatar or robot driven by a human operator. Our strategy leverages a set of human-recorded examples as the core for generating information-rich kinematic signals. To ensure successful object grasping, we propose a deep reinforcement learning strategy. We validate our approach using an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task.