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In recent years, there has been a significant effort dedicated to developingefficient, robust, and general human-to-robot handover systems. However, thearea of flexible handover in the context of complex and continuous objects'motion remains relatively unexplored. In this work, we propose an approach foreffective and robust flexible handover, which enables the robot to grasp movingobjects with flexible motion trajectories with a high success rate. The keyinnovation of our approach is the generation of real-time robust grasptrajectories. We also design a future grasp prediction algorithm to enhance thesystem's adaptability to dynamic handover scenes. We conduct one-motionhandover experiments and motion-continuous handover experiments on our novelbenchmark that includes 31 diverse household objects. The system we havedeveloped allows users to move and rotate objects in their hands within arelatively large range. The success rate of the robot grasping such movingobjects is 78.15% over the entire household object benchmark.

Flexible Handover with Real-Time Robust Dynamic Grasp Trajectory Generation