  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.
