Exploiting a Statistical Body Model for Handover Interaction Primitives

When humans perform object handovers, the non-verbal communication implicit in the movement of the interaction partners mutually communicates information on how the handover will proceed. This intention communication allows both subjects to understand where the transfer of the object will occur, the speed of the gesture, and how careful the receiver of the object must be. In human-robot interaction, it is also desirable that the robot can read and transmit the same information. Bayesian Interaction Primitives (BIP) can be used to learn natural handover interactions from demonstrations performed between humans. In this work, we explore BIPs for handover interactions and compare a state representation obtained directly from a motion capture system with a representation using a statistical body pose model fitted to the motion capture data.