Data-driven architecture to encode information in the kinematics of robots and artificial avatars
This addresses the challenge of making human-operated robots and avatars convey specific information through kinematics, but appears incremental as it builds on existing control methods.
The authors tackled the problem of encoding information like emotions into robot or avatar movements by developing a data-driven control architecture, and validated it on a reach-to-grasp dataset from a pick-and-place task.
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.