Learning Dynamic Robot-to-Robot Object Handover

Abstract

Object handover is an essential skill for collaborative robots in both services robotics and manufacturing scenarios. Most previous works were conducted from the perspective of human-robot interaction. The object handover between robots for collaborative task execution, aiming at optimizing time efficiency with human-like smooth behaviour, has not been extensively addressed. In this work, we propose a skill framework based on variable impedance control and dynamic motion primitives to optimize not only the motion trajectories and variable impedance, but also the timing of hand actions during dynamic motion. The effectiveness of the proposed framework is evaluated on a real dual-arm robot system under two handover scenarios with different constraints on the timing of hand actions. The experiment results demonstrate significant time efficiency improvement with reduction of the execution time by 9.4% and 23.7%, compared with accelerating the motion speed of the demonstrated handover. Furthermore, it can be observed that the robot successfully learned dynamic object handover without requiring transfer action to be triggered after both hands stop and remain still. In addition, in the second experiment, it is shown that the object can be transferred even without ensuring firm contact, which indicates that object handover is possible to be realized by throwing-like motion.