This work explores the effect of object weight on human motion and grip release during handovers to enhance the naturalness, safety, and efficiency of robot-human interactions. We introduce adaptive robotic strategies based on the analysis of human handover behavior with varying object weights. The key contributions of this work includes the development of an adaptive grip-release strategy for robots, a detailed analysis of how object weight influences human motion to guide robotic motion adaptations, and the creation of handover-datasets incorporating various object weights, including the YCB handover dataset. By aligning robotic grip release and motion with human behavior, this work aims to improve robot-human handovers for different weighted objects. We also evaluate these human-inspired adaptive robotic strategies in robot-to-human handovers to assess their effectiveness and performance and demonstrate that they outperform the baseline approaches in terms of naturalness, efficiency, and user perception.

翻译：本研究探讨了物体重量对人类交接过程中的运动及抓握释放的影响，旨在提升人机交互的自然性、安全性与效率。基于对不同重量物体下人类交接行为的分析，我们提出了适应性机器人策略。本工作的主要贡献包括：开发了一种机器人自适应抓握释放策略，详细分析了物体重量如何影响人类运动以指导机器人运动调整，并创建了包含多种物体重量的交接数据集（包括YCB交接数据集）。通过使机器人的抓握释放和运动与人类行为保持一致，本研究旨在改进针对不同重量物体的人机交接。我们还在机器人向人类交接的场景中评估了这些受人类启发的适应性机器人策略，以检验其有效性和性能，结果表明它们在自然性、效率及用户感知方面均优于基线方法。