Learning to navigate cloth using haptics
Abstract
We present a controller that allows an armlike manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.
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The ability to distinguish the inner surface of an article from the outer helps to avoid becoming tangled in the article.
“The ability to distinguish the inner surface of an article from the outer helps to avoid becoming tangled in the article [230].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [230]
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Probabilistic real-time user posture tracking for personalized robot-assisted dressing
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“Clegg et al. (2017) [230] penalise the distance to the goal and the failure of the task.”
BackgroundMethodInfluential
- Authors
- Clegg, A., Yu, W., Erickson, Z., Tan, J., Liu, C. K., & Turk, G.
- Affiliations
- Georgia Institute of Technology (US)
- Google (United States) (US)
- Venue
- 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 2799-2805
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W2604272735
- Cited by
- 20
- References
- 31
- Citations by year
- 2024: 2 · 2023: 6 · 2022: 3 · 2021: 1 · 2020: 2 · 2019: 4 · 2018: 2
- FWCI
- 3.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- fabric manipulation, haptic feedback, reinforcement learning
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