Estimation of Human Cloth Topological Relationship using Depth Sensor for Robotic Clothing Assistance
Open accessAbstract
In this study we consider the problem of estimating Human-Cloth topological relationship using a depth sensor and its application to robotic clothing assistance. In the past, reinforcement learning with low dimensional topological representations has been used to learn the necessary motor skills to perform clothing. In this framework, motion capture system was used to observe the Human-Cloth relationship. There were problems faced with the use of motion capture system: 1) Elaborate and expensive setup of the system 2) Occlusion of optical markers by other objects in the environment 3) Observation of non existent markers due to unwanted reflections. To overcome these difficulties, we propose a framework to observe the Human-Cloth topological relationship using a depth sensor. We demonstrate that the depth sensor can provide reliable estimates of topology coordinates and can replace the complex and expensive setup of motion capture system.
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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 [310]
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“…Vision and Scene Understanding Representations, Data structures and transforms ; J.3 [Computer Applications]: Life and Medical Sciences Health General Terms Algorithms, Human Factors, Measurement Keywords Robotic Clothing Assistance, Depth Sensor, Human-Cloth relationship, Topology…”
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- Authors
- Koganti, N., Tamei, T., Matsubara, T., & Shibata, T.
- Affiliations
- Nara Institute of Science and Technology (JP)
- Venue
- Proceedings of Conference on Advances In Robotics
- Publisher
- ACM
- Published
- 2013-07-04
- Pages
- 1-6
- OpenAlex
- W2152217676
- Cited by
- 21
- References
- 10
- Citations by year
- 2024: 2 · 2023: 2 · 2022: 3 · 2021: 1 · 2020: 1 · 2019: 1 · 2018: 1 · 2017: 6 · 2016: 2 · 2015: 1 · 2014: 1
- FWCI
- 1.82
- Type
- conference paper
- Language
- en
- Open access
- green
- naist.repo.nii.ac.jp
- Locations
- Advances in Robotics - International Conference of The Robotics Society (AIR) (publishedVersion)
- Institutional Repositories DataBase (IRDB) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- depth sensors, topological representation, motion capture alternative, marker occlusion, wearable robotics
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