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.

翻译：摘要：近年来，大量研究致力于开发高效、鲁棒且通用的机器人与人类之间的物体交接系统。然而，在复杂且连续物体运动场景下的柔性交接领域仍相对未被充分探索。本文提出了一种高效鲁棒的柔性交接方法，使机器人能够以高成功率抓取具有灵活运动轨迹的移动物体。该方法的核心创新在于实时生成鲁棒抓取轨迹。同时，我们设计了一种未来抓取预测算法，以增强系统对动态交接场景的适应性。基于包含31种多样化家用物品的新型基准测试集，我们开展了单次运动交接实验与连续运动交接实验。开发的系统允许用户在手中较大范围内移动和旋转物体，在整个家用物品基准测试中，机器人成功抓取此类移动物体的成功率达到78.15%。