The DOI matches its Crossref record; 36 of the 38 references it cites were found in a registry or an index.
Learning force-based manipulation of deformable objects from multiple demonstrations
Abstract
Manipulation of deformable objects often requires a robot to apply specific forces to bring the object into the desired configuration. For instance, tightening a knot requires pulling on the ends, flattening an article of clothing requires smoothing out wrinkles, and erasing a whiteboard requires applying downward pressure. We present a method for learning force-based manipulation skills from demonstrations. Our approach uses non-rigid registration to compute a warping function that transforms both the end-effector poses and forces in each demonstration into the current scene, based on the configuration of the object. Our method then uses the variation between the demonstrations to extract a single trajectory, along with time-varying feedback gains that determine how much to match poses or forces. This results in a learned variable-impedance control strategy that trades off force and position errors, providing for the right level of compliance that applies the necessary forces at each stage of the motion. We evaluate our approach by tying knots in rope, flattening towels, and erasing a whiteboard.
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“Lee et al. (2015a) [118] extend the work to force control, while Lee et al. (2015b) [119] replace TPS-RPM with Coherent Point Drift to improve the transferring quality.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [118]
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Recent Advances in Robot Learning from Demonstration
Ravichandar, H., Polydoros, A. S., Chernova, S., & Billard, A. (2020) · Annu. Rev. Control. Robotics Auton. Syst. · doi:10.1146/annurev-control-100819-063206 · Cited by 947
Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
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Self-citationLearning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
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An algorithmic perspective on imitation learning
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Metadata
- Authors
- Lee, A. X., Lu, H., Gupta, A., Levine, S., & Abbeel, P.
- Affiliations
- University of California, Berkeley (US)
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 177-184
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1851340573
- Cited by
- 140
- References
- 38
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 12 · 2023: 13 · 2022: 13 · 2021: 14 · 2020: 22 · 2019: 17 · 2018: 18 · 2017: 11 · 2016: 8 · 2015: 3
- FWCI
- 18.67
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency
- Air Force Office of Scientific Research
- 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 objects, imitation learning, non-rigid registration, warping function, variable impedance control, compliance, trajectory extraction, time-varying feedback gains, knot tying
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