Learning garment manipulation policies toward robot-assisted dressing.
File(s)
Accepted version
Author(s)
Zhang, Fan
Demiris, Yiannis
Type
Journal Article
Abstract
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's 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%.
Date Issued
2022-04-06
Date Acceptance
2022-03-10
Citation
Science Robotics, 2022, 7 (65), pp.eabm6010-eabm6010
ISSN
2470-9476
Publisher
American Association for the Advancement of Science
Start Page
eabm6010
End Page
eabm6010
Journal / Book Title
Science Robotics
Volume
7
Issue
65
Copyright Statement
© 2022 Owner. This is the author’s version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science Robotics on vol 7, 06/04/2022, DOI: 10.1126/scirobotics.abm6010
Sponsor
Royal Academy Of Engineering
Engineering & Physical Science Research Council (E
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/35385294
Grant Number
CiET1718\46
C19R10637/F19R10853
Subjects
Activities of Daily Living
Bandages
Clothing
Disabled Persons
Humans
Policy
Robotics
Publication Status
Published
Coverage Spatial
United States