Dataset Progress Notice: Processing of this dataset for public release is still in progress. It will be expanded with additional demonstrations on a rolling basis in the coming months. To be transparent, the entire dataset is being handled by two people (first author and mocap tech) on a part-time basis. We realistically just don't have the means to progress any faster. We thank you for your patience and hope that anyone interested can still start playing around with any of the available results!
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Despite the demand for robots in high-value clinical tasks like bathing, contemporary systems still lack the safety and reliability required for complex, sustained physical interaction with humans. A key challenge hindering the development of such systems is that collecting, understanding, and effectively transferring highly dynamic, contact-rich human bathing demonstrations is difficult, even with modern motion and tactile sensing equipment. We present a straightforward, but effective framework for doing so with high fidelity by utilizing contact regions as a key processing primitive. We use our framework to build a dataset of bathing demonstrations performed by trained clinicians on human subjects. We then use this dataset to design and control an arm-mounted dexterous soft hand to perform bathing tasks on a mannequin using open- and closed-loop strategies. Our dataset is the first to provide high quality synchronized motion, shape, contact, and force during sustained, contact-rich human-human interaction, and our transfer strategies demonstrate effective use of these data across multiple levels of the robotics stack. All relevant materials will be publicly released to enable further advancements in physical human-robot interaction (pHRI) research.
We thank Justin C. Macey for assistance with data capture and cleanup, as well as Giorgio Becherini, Shashank Tripathi, and Alpár Cseke for help in setting up the MoSh++ solver. Research reported in this publication was partially supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under award number R01EB036842 and an award from the RAI Institute.
If you find this work useful, please cite our RSS 2026 paper:
@inproceedings{lakshmipathy2026bathing,
title = {High Fidelity Capture, Reconstruction, and Transfer of Human Demonstrations for Robot-Assisted Bathing},
author = {Lakshmipathy, Arjun S. and King, Jonathan P. and Zuo, Ethan and Satishkumar, Rohit and Chen, Hongyi and Ichnowski, Jeffrey and Ding, Dan and Erickson, Zackory and Pollard, Nancy S.},
booktitle = {Proceedings of Robotics: Science and Systems (RSS)},
year = {2026}
}