Visuospatial foresight for physical sequential fabric manipulation
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Expert-script demonstrators have seen substantial use in cloth-shaping systems and knot-tying systems.
“Such demonstrators have seen substantial use in cloth-shaping systems [21,29,38] and knot-tying systems [92].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [38]
PlaNet has been applied in cloth-shaping literature but has so far shown poor performance, likely due to blurry observation prediction.
“PlaNet has been applied in the cloth-shaping literature (Section 6.1.2), but it has so far shown poor performance, likely due to the blurry observation prediction [38,179].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [38]
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 [38]
It often requires training the model with a large amount of data (more than 100k observation-action pairs).
“It often requires training the model with a large amount of data (more than 100k observation–action pairs) [38,233,234].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [38]
Cloth flattening and cloth folding can be trained jointly using a uniform goal-conditioned data-driven approach owing to their identical underlying dynamics and similar state, action, goal and policy representations.
“Cloth flattening and cloth folding can be trained jointly using a uniform goal-conditioned data-driven approach [38,226,229] owing to their identical underlying dynamics as well as the similar state, action, goal and policy representations.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [38]
Only Lee et al. (2021) collect real-world data; most learning-based methods operate in simulated environments, where some apply domain randomisation to achieve Sim2Real.
“Whilst only Lee et al. (2021) [229] collect real-world data, most learning-based methods operate in simulated environments, where some apply domain randomisation [81] to achieve Sim2Real [38,227,233,237].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [38]
Hoque et al. (2022a) propose an effective dense reward function based on the coverage difference between two consecutive states for the cloth-flattening task; their method rewards performance when reaching 92% coverage and penalises misgrasping failures and out-of-boundary scenarios.
“Hoque et al. (2022a) [38] propose an effective dense reward function based on the coverage difference between two consecutive states for the cloth-flattening task; their method also rewards performance when reaching 92% coverage and penalises the misgrasping failures and out-of-boundary scenarios.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [38]
Simulation-trained policies are affected because modern simulators cannot accurately emulate the interaction between the gripper and the CDO.
“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 [38]
In robotic applications using RL, the reward and the goal signal of cloth flattening are usually assigned based on the coverage area of the fabric in its vision input.
“In robotic applications using RL, the reward and the goal signal of cloth flattening are usually assigned based on the coverage area of the fabric in its vision input [38].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [38]
For cloth-flattening tasks, normalised improvement and normalised coverage are the most reliable metrics that can be automated both in simulation and reality; however, this metric does not explicitly include information on wrinkles on the cloth, which is crucial for evaluation when a trial is nearly successful.
“For cloth-flattening tasks, normalised improvement [179] and normalised coverage [38,179] are the most reliable metrics that can be automated both in simulation and reality; however, this metric does not explicitly include information on wrinkles on the cloth, which is crucial for evaluation when a trial is nearly successful.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [38]
Although there have been several attempts, designing a simple and effective dense reward function for cloth folding and dressing tasks is still challenging.
“Although there have been several attempts [1,31,38], designing a simple and effective dense reward function for cloth folding and dressing tasks is still challenging.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [38]
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Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 19 · see citing works
“Hoque et al. (2022a) [38] propose an effective dense reward function based on the coverage difference between two consecutive states for the cloth-flattening task; their method also rewards performance when reaching 92% coverage and penalises the misgrasping failures and out-of-boundary scenarios.”
BackgroundMethodInfluentialGoal-Conditioned Model Simplification for 1-D and 2-D Deformable Object Manipulation
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“For instance, Hoque et al. [42] demonstrated a visual dynamics model trained on domain-randomized RGBD images for fabric folding tasks, while Li et al. [36] used a recurrent state-space model for latent dynamics modeling.”
- Authors
- Hoque, R., Seita, D., Balakrishna, A., Ganapathi, A., Tanwani, A. K., Jamali, N., Yamane, K., Iba, S., & Goldberg, K.
- Affiliations
- University of California, Berkeley (US)
- Honda Research Institute, USA (US)
- Corresponding author
- Hoque, Ryan
- ORCID
- Venue
- Autonomous Robots
- Published
- 2021-07-26
- Volume
- 46
- Issue
- 1
- Pages
- 175-199
- ISSN
- 0929-5593 (ISSN-L)
- 1573-7527
- OpenAlex
- W3184955535
- Cited by
- 45
- References
- 80
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 13 · 2023: 7 · 2022: 11 · 2021: 1
- FWCI
- 3.56
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Autonomous Robots (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1734633)
- Cisco Systems
- Nvidia
- Intuitive Surgical
- 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 folding, goal-conditioned policies, domain randomization, depth sensing, surgical robots, da Vinci Research Kit, sim-to-real transfer
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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