Cloth dynamics modeling in latent spaces and its application to robotic clothing assistance
Abstract
Real-time estimation of human-cloth relationship is crucial for efficient learning of motor skills in robotic clothing assistance. However, cloth state estimation using a depth sensor is a challenging problem with inherent ambiguity. To address this problem, we propose the offline learning of a cloth dynamics model by incorporating reliable motion capture data and applying this model for the online tracking of human-cloth relationship using a depth sensor. In this study, we evaluate the performance of using a shared Gaussian Process Latent Variable Model in learning the dynamics of clothing articles. The experimental results demonstrate the effectiveness of shared GP-LVM in capturing cloth dynamics using few data samples and the ability to generalize to unseen settings. We further demonstrate three key factors that affect the predictive performance of the trained dynamics model.
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Robust real-time estimation of topological relationships between body and cloth is essential for real-world trials.
“Robust real-time estimation [300,310,311,312] of such topological relationships is essential for real-world trials.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [312]
Reliably finding the topological correspondence between the different parts of the body and the garment is a challenge for perception.
“Reliably finding the topological correspondence between the different parts of the body and the garment is a challenge for perception [246,312].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [312]
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Probabilistic real-time user posture tracking for personalized robot-assisted dressing
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“Reliably finding the topological correspondence between the different parts of the body and the garment is a challenge for perception [246,312].”
BackgroundActive Boundary Component Models for robotic dressing assistance
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- Authors
- Koganti, N., Ngeo, J. G., Tomoya, T., Ikeda, K., & Shibata, T.
- Affiliations
- Nara Institute of Science and Technology (JP)
- Kyushu Institute of Technology (JP)
- Venue
- 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2015-09
- Pages
- 3464-3469
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W2215479383
- Cited by
- 23
- References
- 19
- Citations by year
- 2023: 2 · 2022: 3 · 2021: 3 · 2020: 3 · 2019: 2 · 2018: 1 · 2017: 6 · 2016: 3
- FWCI
- 4.67
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- Funders
- Japan Society for the Promotion of Science
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Materials Science › Polymers and Plastics › Textile materials and evaluations
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- Gaussian process latent variable model, depth sensors, motion capture, offline learning, online tracking, few-shot learning
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