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
Cites
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Saxena, A., Driemeyer, J., & Ng, A. Y. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364907087172 · Cited by 950
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Kemp, C. C., Edsinger, A., & Torres-Jara, E. (2007) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2007.339604 · Cited by 378
Anisotropic Huber-L1 Optical Flow
Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., & Bischof, H. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.108 · Cited by 354
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Moll, M., & Kavraki, L. E. (2006) · IEEE Transactions on Robotics · doi:10.1109/tro.2006.878933 · Cited by 205
Wyrobek, K. A., Berger, E., der Loos, H. F. M. V., & Salisbury, J. K. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543527 · Cited by 191
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Research works printed but not linked: 1
- wyrobek, 2008, Towards a personal robot, Proc ICRA
Cited by
Sorted by citations
Deep learning for detecting robotic grasps
Lenz, I., Lee, H., & Saxena, A. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364914549607 · Cited by 1,683
Data-Driven Grasp Synthesis—A Survey
Bohg, J., Morales, A., Asfour, T., & Kragic, D. (2014) · IEEE Transactions on Robotics · doi:10.1109/tro.2013.2289018 · Cited by 929
Dual arm manipulation—A survey
Smith, C., Karayiannidis, Y., Nalpantidis, L., Gratal, X., Qi, P., Dimarogonas, D. V., & Kragić, D. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.07.005 · Cited by 562
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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