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Cited by 142Robot Manipulation and LearningReinforcement Learning in RoboticsSoft Robotics and Applications

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

Lee, Alex X., Lu, Henry, Gupta, Abhishek, Levine, Sergey, Abbeel, Pieter·2015·2015 IEEE International Conference on Robotics and Automation (ICRA)
DOI 10.1109/icra.2015.7138997
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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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  1. o'hagan, 1994, Bayesian Inference
  2. 10.1109/IROS.2011.6048873 · DOI

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Metadata

Authors
Lee, A. X., Lu, H., Gupta, A., Levine, S., & Abbeel, P.
Affiliations
University of California, Berkeley (US)
ORCID
Venue
2015 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Published
2015-05
Pages
177-184
ISSN
1050-4729 (ISSN-L)
DOI
10.1109/icra.2015.7138997
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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