Garment diffusion models for robot-assisted dressing
File(s)
Accepted version
Author(s)
Kotsovolis, Stelios
Demiris, Yiannis
Type
Journal Article
Abstract
Robots have the potential to assist people with disabilities and the elderly. One of the most common and burdensome tasks for caregivers is dressing. Two challenges of robot-assisted dressing are modeling the dynamics of garments and handling visual occlusions that obstruct the perception of the full state of the garment due to the proximity between the garment, the robot, and the human. In this letter, we propose a diffusion-based dynamics model for garments during robot-assisted dressing that can deal with partial point cloud observations. The diffusion model, conditioned on the observation and the robot's action, is used to predict a full point cloud of the garment's opening of the future state. The model is utilized in a model predictive controller, that is trained iteratively with model-based reinforcement learning. In our experiments, we examine a common problem of dressing: the insertion of a garment's sleeve on an arm. As demonstrated by the performed experiments, the proposed diffusion-based model predictive controller can be effectively used for robot-assisted dressing and handle visual occlusions. Moreover, our approach is highly sample-efficient. Specifically, the controller achieved 91.2% success rate in the examined dressing task with less than 100 sampled trajectories. Real-wold experiments demonstrate that the proposed method can adapt to the sim-to-real gap and generalize well to novel garments and configurations of the body.
Date Issued
2025-02-01
Date Acceptance
2024-11-25
Citation
IEEE Robotics and Automation Letters, 2025, 10 (2), pp.1217-1224
ISSN
2377-3766
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1217
End Page
1224
Journal / Book Title
IEEE Robotics and Automation Letters
Volume
10
Issue
2
Copyright Statement
Copyright ©2024 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Subjects
0913 Mechanical Engineering
Publication Status
Published
Date Publish Online
2024-12-16