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Data-driven architecture to encode information in the kinematics of robots and artificial avatars

Francesco De Lellis, Marco Coraggio, Nathan C. Foster, Riccardo Villa, Cristina Becchio, Mario di Bernardo

发表年份
2024
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摘要

We present a data-driven control architecture for modifying the kinematics of robots and artificial avatars to encode specific information such as the presence or not of an emotion in the movements of an avatar or robot driven by a human operator. We validate our approach on an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task.

关键词

eess.SYcs.LGcs.RO

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