YCB-Handovers Dataset: Analyzing Object Weight Impact on Human Handovers to Adapt Robotic Handover Motion

TL;DR

This paper presents the YCB-Handovers dataset, capturing human motion during object handovers with varying weights to inform adaptive robotic handover strategies and improve human-robot collaboration.

Contribution

The paper introduces a comprehensive dataset of human handovers with varied object weights, linking object weight to human motion patterns for robotic application insights.

Findings

Object weight significantly affects human reaching motion.

The dataset enables analysis of weight-sensitive handover behaviors.

Insights support development of adaptive robotic handover motions.

Abstract

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.