Sequential human assembly and disassembly motions in human-robot coexisting environments

As human-robot systems and autonomous robots become increasingly
prevalent, the need for task-oriented datasets to study human behaviors in
shared spaces has grown significantly. We present a novel dataset focusing
on sequential human assembly and disassembly motions in human-robot
coexisting environments. It contains over 10,000 samples recorded from
multi-view camera setups, each comprising synchronized RGB videos and 2D
and 3D human skeletons. Data were collected from 33 participants with
diverse physical characteristics and behavior preferences. This dataset
highlights practical challenges such as partial occlusions, similar
repetitive motions, and varying human behaviors, which are often
overlooked in existing datasets and research. Technical validation using
benchmarking with state-of-the-art deep learning models reveals
significant potential in using this dataset for practical applications. To
support diverse research applications, this dataset provides raw and
processed data with detailed annotations, including precise timestamps,
procedure annotations, and Python codes for reproducibility. It aims to
advance research in human motion prediction, task-oriented robotic
sequential decision-making, motion and task planning of autonomous robots,
and human-robot collaborative policies.