VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
Open accessAbstract
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery.Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but related tasks.We extend the Visual Foresight framework to learn fabric dynamics that can be efficiently reused to accomplish different fabric manipulation tasks with a single goal-conditioned policy.We introduce VisuoSpatial Foresight (VSF), which builds on prior work by learning visual dynamics on domain randomized RGB images and depth maps simultaneously and completely in simulation.We experimentally evaluate VSF on multi-step fabric smoothing and folding tasks against 5 baseline methods in simulation and on the da Vinci Research Kit (dVRK) surgical robot without any demonstrations at train or test time.Furthermore, we find that leveraging depth significantly improves performance.RGBD data yields an 80% improvement in fabric folding success rate over pure RGB data.Code, data, videos, and supplementary material are available at https://sites.google.com/view/fabric-vsf/.
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Training an approximated dynamic model using supervised learning with transitions induced by a base policy generally does not work well in practice, especially in high-dimensional settings, mainly due to the distributional shift problem.
“This approach generally does not work well in practice, especially in high-dimensional settings [37,38], mainly due to the distributional shift problem, i.e., ρπP^(st)≠ρπbase(st)[8]”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [37]
Section IV-C (Data Generation; page unavailable) says the dynamics model was trained on transitions from a random policy, and Section IV-E (VisuoSpatial Foresight; page unavailable) says MPC mitigates compounding model error. Neither passage addresses the distributional shift between a base policy’s state distribution and the learned model’s policy distribution. A search for “distributional shift” and base-policy distribution mismatch found no passage stating the cited point. (LLM, conf=0.73)
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Cited by
Sorted by citations
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Self-citationUntangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
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“Domain randomisation, such as randomising the background colour of the table [37,233], is used for transferring the simulation-trained end-to-end policies to the real world (Section 3.4).”
BackgroundMethodInfluentialDisentangling Dense Multi-Cable Knots
Viswanath, V., Grannen, J., Sundaresan, P., Thananjeyan, B., Balakrishna, A., Novoseller, E., Ichnowski, J., Laskey, M., Gonzalez, J. E., & Goldberg, K. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636397 · Cited by 21 · see citing works
Self-citation
- Authors
- Hoque, R., Seita, D., Balakrishna, A., Ganapathi, A., Tanwani, A., Jamali, N., Yamane, K., Iba, S., & Goldberg, K.
- ORCID
- Venue
- Robotics: Science and Systems XVI
- Published
- 2020-07-12
- OpenAlex
- W3039258510
- Cited by
- 74
- References
- 69
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 12 · 2023: 16 · 2022: 20 · 2021: 16 · 2020: 4
- FWCI
- 21.00
- 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 (1734633)
- Cisco Systems
- Nvidia
- Intuitive Surgical
- National Physical Science Consortium
- Toyota Research Institute
- Amazon Robotics
- Honda Research Institute, USA
- Samsung
- 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
- fabric manipulation, goal-conditioned policies, domain randomization, depth images, surgical robots, da Vinci Research Kit, fabric folding, sim-to-real transfer
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