The DOI matches its Crossref record; 70 of the 76 references it cites were found in a registry or an index.
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks
Open accessAbstract
Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a long-standing challenge in robotic manipulation. The complex dynamics and high-dimensional configuration spaces of deformables, compared to rigid objects, make manipulation difficult not only for multi-step planning, but even for goal specification. Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag". In this work, we develop a suite of simulated benchmarks with 1D, 2D, and 3D deformable structures, including tasks that involve image-based goal-conditioning and multi-step deformable manipulation. We propose embedding goal-conditioning into Transporter Networks, a recently proposed model architecture for learning robotic manipulation that rearranges deep features to infer displacements that can represent pick and place actions. We demonstrate that goal-conditioned Transporter Networks enable agents to manipulate deformable structures into flexibly specified configurations without test-time visual anchors for target locations. We also significantly extend prior results using Transporter Networks for manipulating deformable objects by testing on tasks with 2D and 3D deformables. Supplementary material is available at https://berkeleyautomation.github.io/bags/.
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In the citing paper
“Seita et al. (2021) [34] extend Transporter Net to the goal-conditioned control domain and apply it to cloth-shaping and bag-manipulation tasks in simulation.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [34]
In the citing paper
“Simulation-trained policies are affected since modern simulators cannot accurately emulate the interaction between the gripper and the CDO [29,34,38].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [34]
In the citing paper
“In simulation, Seita et al. (2021a) [34] achieve a 63.3% success rate out of 60 trials for sack opening and a 51.7% success rate for inserting one item in simulation.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [34]
In the citing paper
“The canonical task of bag manipulation is item loading [238] which involves opening the bag, inserting items [34] (see Figure 8b) and lifting the bag.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [34]
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Metadata
- Authors
- Seita, D., Florence, P., Tompson, J., Coumans, E., Sindhwani, V., Goldberg, K., & Zeng, A.
- Affiliations
- University of California, Berkeley (US)
- Google (United States) (US)
- Venue
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2021-05-30
- Pages
- 4568-4575
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3207181464
- Cited by
- 130
- References
- 76
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 32 · 2023: 32 · 2022: 29 · 2021: 11
- FWCI
- 38.75
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- deformable object manipulation, robotic manipulation, cables, textiles, bags, pick-and-place, displacement estimation
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