Data-driven architecture to encode information in the kinematics of robots and artificial avatars
<|reference_start|>Data-driven architecture to encode information in the kinematics of robots and artificial avatars: 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.<|reference_end|>
@article{de lellis2024data-driven,
    title={Data-driven architecture to encode information in the kinematics of
  robots and artificial avatars},
    author={Francesco De Lellis, Marco Coraggio, Nathan C. Foster, Riccardo Villa,
  Cristina Becchio, Mario di Bernardo},
    journal={arXiv preprint arXiv:2403.06557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06557},
    primaryClass={eess.SY cs.LG cs.RO cs.SY}
}