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2 results for “Object Handovers”
Multi-sensor Dataset of Multiple Sequential Human-to-Human Object Handovers in Shelving and Un-shelving Tasks
<p>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 consists of the position and orientation trajectories of 13 upper-body bones of the giver and the receiver and position trajectories of the 27 markers placed on their upper bodies, all recorded at 120Hz. 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 correct grasps.<br><br>The details about the dataset collection procedure are available in the article:<br>Kshirsagar, A., Fortuna, R., Xie, Z., & Hoffman, G. (2025). Descriptor: Multi-sensor Dataset of Multiple Sequential Human-to-Human Object Handovers in Shelving and Un-shelving Tasks (MH2HO). IEEE Data Descriptions. (http://dx.doi.org/10.1109/IEEEDATA.2025.3580058)</p>
Dataset of Bimanual Human-to-Human Object Handovers
<p>We present a multi-sensor dataset of bimanual human-to-human object handovers. The dataset consists of 240 recordings obtained from 12 pairs of participants performing bimanual object handovers with 10 objects, and 120 recordings obtained from the same 12 pairs of participants performing unimanual handovers with 5 of those objects. Each recording includes the giver and receiver's 13 upper-body bone position and orientation trajectories, position trajectories for the 27 markers placed on their upper bodies, object position and orientation trajectories, and two RGB-D data streams. The motion trajectories are recorded at 120Hz and the RGB-D streams are recorded at 30Hz. The recordings are annotated with the three handover phases: reach, transfer, and retreat. The dataset also includes four anthropometric measurements of the participants: height, waistline height, arm span, and weight. Our dataset could help investigations of the bimanual reaching motions and grasps utilized by humans while performing handovers. Also, it can be used to train robots to perform bimanual object handovers with humans.</p> <p>More details about the dataset can be found in this paper: https://doi.org/10.1016/j.dib.2023.109277</p>
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