Abstract

We provide a multi-sensor dataset containing RGB-D and motion tracking data from sequential human-to-human object handovers. We recorded 12 pairs of participants executing shelving and un-shelving tasks involving 30 object handovers, resulting in 1440 handovers. Each recording includes the position trajectories of 27 markers placed on the upper bodies of both the giver and the receiver, recorded at 120 Hz, as well as the position and orientation trajectories of 13 upper-body bones, which are estimated from the marker data. The recordings also include two RGB-D data streams at 30Hz. We also provide four anthropometric measurements of the participants: height, waistline height, arm span, and weight. The dataset is valuable for investigating the body movements, grasps, and coordination strategies utilized by humans while performing tasks such as shelving which involve multiple sequential object handovers. Additionally, the dataset can be used to teach robots perform tasks involving object handovers with people, as well as self-handovers to adjust grasps.

The dataset is available on Zenodo.