Real-time estimation of Human-Cloth topological relationship using depth sensor for robotic clothing assistance
Abstract
In this study, we propose a novel method for the real-time estimation of Human-Cloth relationship, which is crucial for efficient motor skill learning in Robotic Clothing Assistance. This system relies on the use of low cost depth sensor, which provides color and depth images without requiring an elaborate setup making it suitable for real-world applications. We present an efficient algorithm to estimate the parameters that represent the topological relationship between human and the clothing article. At the core of our approach are low dimensional representation of Human-Cloth relationship using topology coordinates for fast learning of motor skills and a unified ellipse fitting algorithm for the compact representation of the state of clothing articles. We conducted experiments that illustrate the robustness of these feature representations. Furthermore, we evaluated the performance of our proposed method by applying it to real-time clothing assistance tasks and compared the estimates provided by our method with the ground truth.
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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 [311]
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Self-citation
- Authors
- Koganti, N., Tamei, T., Matsubara, T., & Shibata, T.
- Affiliations
- Nara Institute of Science and Technology (JP)
- Kyushu Institute of Technology (JP)
- Venue
- The 23rd IEEE International Symposium on Robot and Human Interactive Communication
- Publisher
- IEEE
- Published
- 2014-08
- Pages
- 124-129
- ISSN
- 1944-9437 (ISSN-L)
- OpenAlex
- W2017270214
- Cited by
- 24
- References
- 16
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 6 · 2023: 6 · 2022: 2 · 2020: 1 · 2019: 1 · 2018: 1 · 2017: 2 · 2015: 1
- FWCI
- 1.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE RO-MAN (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- depth sensors, real-time estimation, ellipse fitting, low-dimensional representation, motor skill learning
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