The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding
Abstract
We present a novel vision-based grasp point detection algorithm that can reliably detect the corners of a piece of cloth, using only geometric cues that are robust to variation in texture. Furthermore, we demonstrate the effectiveness of our algorithm in the context of folding a towel using a general-purpose two-armed mobile robotic platform without the use of specialized end-effectors or tools. The robot begins by picking up a randomly dropped towel from a table, goes through a sequence of vision-based re-grasps and manipulations-partially in the air, partially on the table-and finally stacks the folded towel in a target location. The reliability and robustness of our algorithm enables for the first time a robot with general purpose manipulators to reliably and fully-autonomously fold previously unseen towels, demonstrating success on all 50 out of 50 single-towel trials as well as on a pile of 5 towels.
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In the citing paper
“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 [9]
In the citing paper
“A robust RMS should be able to recognise the failing states and recover from these failures [9,36].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [9]
Citations
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Cited by
Sorted by citations
Deep learning for detecting robotic grasps
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Data-Driven Grasp Synthesis—A Survey
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Robotic grasp detection using deep convolutional neural networks
Kumra, S., & Kanan, C. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202237 · Cited by 566
1Data-Driven Grasp Synthesis- A SurveyPreprint
Bohg, J., Morales, A., Asfour, T., & Kragić, D. (2014) · Cited by 532
Metadata
- Authors
- Maitin-Shepard, J., Cusumano-Towner, M., Lei, J., & Abbeel, P.
- Affiliations
- University of California, Berkeley (US)
- Venue
- 2010 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2010-05
- Pages
- 2308-2315
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2089630413
- Cited by
- 418
- References
- 29
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 30 · 2023: 29 · 2022: 32 · 2021: 21 · 2020: 37 · 2019: 26 · 2018: 31 · 2017: 29 · 2016: 25 · 2015: 30 · 2014: 29 · 2013: 19 · 2012: 18
- FWCI
- 43.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- multi-view geometry, geometric cues, autonomous manipulation
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