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
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.