分析物体重量对人类交接动作的影响，助力机器人自适应规划。
YCB-Handovers Dataset: Analyzing Object Weight Impact on Human Handovers to Adapt Robotic Handover Motion
- 构建2771次人体交接数据集，涵盖多种物体重量变化。
- 发现物体重量显著影响人类伸手动作的轨迹与速度。
- 适合研究人机协作、机器人自适应抓取与交接的团队使用。
本文提出YCB-Handovers数据集，记录了2771次不同物体重量下的人类间交接动作。该数据集旨在填补人机协作研究中的空白，为理解物体重量对交接行为及准备信号的影响提供依据，支持直观的机器人运动规划。其基础为广泛使用的YCB（Yale-CMU-Berkeley）数据集，该数据集是机器人操作算法中常用的标准数据集，涵盖抓取与搬运任务。本数据集引入人体交接动作模式，适用于数据驱动的人类启发式建模，实现对重量敏感的运动规划与自适应机器人行为。数据覆盖广泛的物体重量范围，支持对手交行为与重量变化关系的深入研究。部分物体需精细交接，与常规交接形成对比。本文还详细分析了物体重量对人类伸手动作的影响。
原文摘要 · Abstract (English)
This paper introduces the YCB-Handovers dataset, capturing motion data of 2771 human-human handovers with varying object weights. The dataset aims to bridge a gap in human-robot collaboration research, providing insights into the impact of object weight in human handovers and readiness cues for intuitive robotic motion planning. The underlying dataset for object recognition and tracking is the YCB (Yale-CMU-Berkeley) dataset, which is an established standard dataset used in algorithms for robotic manipulation, including grasping and carrying objects. The YCB-Handovers dataset incorporates human motion patterns in handovers, making it applicable for data-driven, human-inspired models aimed at weight-sensitive motion planning and adaptive robotic behaviors. This dataset covers an extensive range of weights, allowing for a more robust study of handover behavior and weight variation. Some objects also require careful handovers, highlighting contrasts with standard handovers. We also provide a detailed analysis of the object's weight impact on the human reaching motion in these handovers.
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