Handovers are basic yet sophisticated motor tasks performed seamlessly by humans. They are among the most common activities in our daily lives and social environments. This makes mastering the art of handovers critical for a social and collaborative robot. In this work, we present an experimental study that involved human-human handovers by 13 pairs, i.e., 26 participants. We record and explore multiple features of handovers amongst humans aimed at inspiring handovers amongst humans and robots. With this work, we further create and publish a novel data set of 8672 handovers, bringing together human motion and the forces involved. We further analyze the effect of object weight and the role of visual sensory input in human-human handovers, as well as possible design implications for robots. As a proof of concept, the data set was used for creating a human-inspired data-driven strategy for robotic grip release in handovers, which was demonstrated to result in better robot to human handovers.

翻译：交接是人类无缝完成的基本但精细的运动任务，也是人类日常生活与社会环境中最普遍的行为之一。因此，掌握交接艺术对于社交型与协作型机器人至关重要。本研究开展了一项包含13组（即26名参与者）人类之间交接的实验研究。我们记录并探索了人类交接的多种特征，旨在为人机交接设计提供灵感。本研究进一步创建并发布了包含8672次交接事件的新型数据集，该数据集整合了人体运动数据与相关作用力信息。此外，我们分析了物体重量与视觉感官输入对人类交接过程的影响，并探讨了对机器人设计的潜在启示。作为概念验证，该数据集被用于构建基于人类行为启发的数据驱动策略，实现机器人在交接过程中的抓握释放控制，实验证明该策略可显著改善机器人与人之间的交接效果。