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