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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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 [9]
A robust RMS should be able to recognise the failing states and recover from these failures.
“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]
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- 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 › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- multi-view geometry, geometric cues, autonomous manipulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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