{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:08:14Z","timestamp":1780704494136,"version":"3.54.1"},"reference-count":86,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"65","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sci. Robot."],"published-print":{"date-parts":[[2022,4,27]]},"abstract":"<jats:p>Assistive robots have the potential to support people with disabilities in a variety of activities of daily living, such as dressing. People who have completely lost their upper limb movement functionality may benefit from robot-assisted dressing, which involves complex deformable garment manipulation. Here, we report a dressing pipeline intended for these people and experimentally validate it on a medical training manikin. The pipeline is composed of the robot grasping a hospital gown hung on a rail, fully unfolding the gown, navigating around a bed, and lifting up the user\u2019s arms in sequence to finally dress the user. To automate this pipeline, we address two fundamental challenges: first, learning manipulation policies to bring the garment from an uncertain state into a configuration that facilitates robust dressing; second, transferring the deformable object manipulation policies learned in simulation to real world to leverage cost-effective data generation. We tackle the first challenge by proposing an active pre-grasp manipulation approach that learns to isolate the garment grasping area before grasping. The approach combines prehensile and nonprehensile actions and thus alleviates grasping-only behavioral uncertainties. For the second challenge, we bridge the sim-to-real gap of deformable object policy transfer by approximating the simulator to real-world garment physics. A contrastive neural network is introduced to compare pairs of real and simulated garment observations, measure their physical similarity, and account for simulator parameters inaccuracies. The proposed method enables a dual-arm robot to put back-opening hospital gowns onto a medical manikin with a success rate of more than 90%.<\/jats:p>","DOI":"10.1126\/scirobotics.abm6010","type":"journal-article","created":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T17:56:02Z","timestamp":1649267762000},"source":"Crossref","is-referenced-by-count":79,"title":["Learning garment manipulation policies toward robot-assisted dressing"],"prefix":"10.1126","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5869-4960","authenticated-orcid":true,"given":"Fan","family":"Zhang","sequence":"first","affiliation":[{"name":"Personal Robotics Laboratory, Department of Electrical and Electronic Engineering, Imperial College London, London, UK."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4917-3343","authenticated-orcid":true,"given":"Yiannis","family":"Demiris","sequence":"additional","affiliation":[{"name":"Personal Robotics Laboratory, Department of Electrical and Electronic Engineering, Imperial College London, London, UK."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12369-013-0218-7"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12369-014-0242-2"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apmr.2007.11.038"},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","unstructured":"A. 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Dabiri Seeing the wind: Visual wind speed prediction with a coupled convolutional and recurrent neural network in Advances in Neural Information Processing Systems (PMLR 2019) pp. 8735\u20138745."},{"key":"e_1_3_2_71_2","doi-asserted-by":"crossref","unstructured":"T. F. Runia K. Gavrilyuk C. G. Snoek A. W. Smeulders Cloth in the wind: A case study of physical measurement through simulation in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (IEEE 2020) pp. 10498\u201310507.","DOI":"10.1109\/CVPR42600.2020.01051"},{"key":"e_1_3_2_72_2","doi-asserted-by":"crossref","unstructured":"A. Allevato E. S. Short M. Pryor A. 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Weinberger Densely connected convolutional networks in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE 2017) pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_83_2","doi-asserted-by":"crossref","unstructured":"T. Hester M. Vecerik O. Pietquin M. Lanctot T. Schaul B. Piot D. Horgan J. Quan A. Sendonaris I. Osband J. Agapiou J. Z. Leibo A. Gruslys Deep q-learning from demonstrations in Proceedings of the AAAI Conference on Artificial Intelligence (PMLR 2018).","DOI":"10.1609\/aaai.v32i1.11757"},{"key":"e_1_3_2_84_2","doi-asserted-by":"crossref","unstructured":"A. Singh L. Yang K. Hartikainen C. Finn S. Levine End-to-end robotic reinforcement learning without reward engineering in Proceedings of Robotics: Science and Systems (2019).","DOI":"10.15607\/RSS.2019.XV.073"},{"key":"e_1_3_2_85_2","unstructured":"A. Xie A. Singh S. Levine C. Finn Few-shot goal inference for visuomotor learning and planning Conference on Robot Learning (CoRL) (PMLR 2018) pp. 40\u201352."},{"key":"e_1_3_2_86_2","doi-asserted-by":"crossref","unstructured":"C. Heindl S. Zambal J. Scharinger Learning to predict robot keypoints using artificially generated images in Proceedings of the 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (IEEE 2019) pp. 1536\u20131539.","DOI":"10.1109\/ETFA.2019.8868243"},{"key":"e_1_3_2_87_2","unstructured":"G. Brockman V. Cheung L. Pettersson J. Schneider J. Schulman J. Tang W. Zaremba Openai gym. arXiv:1606.01540 [cs.LG] (5 June 2016)."}],"container-title":["Science Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/scirobotics.abm6010","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T18:24:48Z","timestamp":1726943088000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.abm6010"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,27]]},"references-count":86,"journal-issue":{"issue":"65","published-print":{"date-parts":[[2022,4,27]]}},"alternative-id":["10.1126\/scirobotics.abm6010"],"URL":"https:\/\/doi.org\/10.1126\/scirobotics.abm6010","relation":{},"ISSN":["2470-9476"],"issn-type":[{"value":"2470-9476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,27]]},"article-number":"eabm6010"}}