Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning
Abstract
We present a novel approach to the problem of autonomously recognizing and unfolding articles of clothing using a dual manipulator. The problem consists of grasping an article from a random point, recognizing it and then bringing it into an unfolded state. We propose a data-driven method for clothes recognition from depth images using Random Decision Forests. We also propose a method for unfolding an article of clothing after estimating and grasping two key-points, using Hough forests. Both methods are implemented into a POMDP framework allowing the robot to interact optimally with the garments, taking into account uncertainty in the recognition and point estimation process. This active recognition and unfolding makes our system very robust to noisy observations. Our methods were tested on regular-sized clothes using a dual-arm manipulator and an Xtion depth sensor. We achieved 100% accuracy in active recognition and 93.3% unfolding success rate, while our system operates faster compared to the state of the art.
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Classical control methods have been successfully applied to CDO manipulation, but they tend to be restricted to a narrow range of defined configurations.
“Classical control methods have been successfully applied to CDO manipulation [9,10], but they tend to be restricted to a narrow range of defined configurations.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [10]
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BackgroundMethodActive Boundary Component Models for robotic dressing assistance
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“These challenges make both planning and perception complex problems, particularly for traditional optimization or search based methods [24], [25], [26].”
- Authors
- Doumanoglou, A., Kargakos, A., Kim, T.-K., & Malassiotis, S.
- Affiliations
- Institute for Infocomm Research (SG)
- Aristotle University of Thessaloniki (GR)
- Imperial College London (GB)
- Information Technology Institute (EG)
- Venue
- 2014 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2014-05
- Pages
- 987-993
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2052461980
- Cited by
- 123
- References
- 26
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 3 · 2023: 9 · 2022: 15 · 2021: 8 · 2020: 8 · 2019: 6 · 2018: 22 · 2017: 13 · 2016: 18 · 2015: 10 · 2014: 4
- FWCI
- 20.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- 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
- dual-arm manipulator, random forest, Hough forest, point estimation, partially observable Markov decision processes, probabilistic planning, depth images, uncertainty
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