Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation
Open accessAbstract
Self-occlusion is challenging for cloth manipulation, as it makes it difficult to estimate the full state of the cloth.Ideally, a robot trying to unfold a crumpled or folded cloth should be able to reason about the cloth's occluded regions.We leverage recent advances in pose estimation for cloth to build a system that uses explicit occlusion reasoning to unfold a crumpled cloth.Specifically, we first learn a model to reconstruct the mesh of the cloth.However, the model will likely have errors due to the complexities of the cloth configurations and due to ambiguities from occlusions.Our main insight is that we can further refine the predicted reconstruction by performing test-time finetuning with self-supervised losses.The obtained reconstructed mesh allows us to use a mesh-based dynamics model for planning while reasoning about occlusions.We evaluate our system both on cloth flattening as well as on cloth canonicalization, in which the objective is to manipulate the cloth into a canonical pose.Our experiments show that our method significantly outperforms prior methods that do not explicitly account for occlusions or perform test-time optimization.Videos and visualizations can be found on our project website.
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Mesh representation of the cloth can bring topological information into planning for grasping and flattening, and plays a significant role in keeping track of the state of the article, especially when there are occlusions in the visual input.
“Mesh representation of the cloth can bring topological information into planning for grasping and flattening, and it plays a significant role in keeping track of the state of the article, especially when there are occlusions in the visual input [179,235,267].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [267]
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Flagged first, then by citations
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Sorted by citations
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Goal-Conditioned Model Simplification for 1-D and 2-D Deformable Object Manipulation
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- Authors
- Huang, Z., Lin, X., & Held, D.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Robotics: Science and Systems XVIII
- Published
- 2022-06-27
- OpenAlex
- W4281765718
- Cited by
- 39
- References
- 62
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 8 · 2023: 15 · 2022: 1
- FWCI
- 14.12
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Robotics: Science and Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1849154, CAREER, 2046491)
- LG Electronics
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- fabric manipulation, self-occlusion, self-supervised learning, robot manipulation planning
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