Adaptive Robot-Human Handovers with Preference Learning

This paper proposes an adaptive method for robot- to-human handovers under different scenarios. The method combines Dynamic Movement Primitives (DMP) with Preference Learning (PL) to generate online trajectories that are reactive to human motion, modulating the speed of the robot. The PL allows for tuning the coupling parameters of the DMP, tailoring the interaction to each participant personally, and allowing for qualitative analysis of user preferences. Simulation of an interaction-constrained learning task with different optimization techniques is performed to determine an appropriate learning approach for a handover task. The validity of the approach is demonstrated through experiments with participants on two handover tasks, with results indicating that the proposed method leads to seamless and pleasurable interactions.