Cited by 114Reinforcement Learning in RoboticsRobot Manipulation and LearningSoft Robotics and Applications

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Dynamic Cloth Manipulation with Deep Reinforcement Learning

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Jangir, Rishabh, Alenya, Guillem, Torras, Carme·2020·2020 IEEE International Conference on Robotics and Automation (ICRA)

DOI 10.1109/icra40945.2020.9196659

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Abstract

In this paper we present a Deep Reinforcement Learning approach to solve dynamic cloth manipulation tasks. Differing from the case of rigid objects, we stress that the followed trajectory (including speed and acceleration) has a decisive influence on the final state of cloth, which can greatly vary even if the positions reached by the grasped points are the same. We explore how goal positions for non-grasped points can be attained through learning adequate trajectories for the grasped points. Our approach uses few demonstrations to improve control policy learning, and a sparse reward approach to avoid engineering complex reward functions. Since perception of textiles is challenging, we also study different state representations to assess the minimum observation space required for learning to succeed. Finally, we compare different combinations of control policy encodings, demonstrations, and sparse reward learning techniques, and show that our proposed approach can learn dynamic cloth manipulation in an efficient way, i.e., using a reduced observation space, a few demonstrations, and a sparse reward.

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Authors: Jangir, R., Alenya, G., & Torras, C.
Affiliations: Institut de Robòtica i Informàtica Industrial (ES)
ORCID: Jangir, Rishabh
Venue: 2020 IEEE International Conference on Robotics and Automation (ICRA)
Publisher: IEEE
Published: 2020-05
Pages: 4630-4636
ISSN: 1050-4729 (ISSN-L)
DOI: 10.1109/icra40945.2020.9196659
OpenAlex: W2982652729
Cited by: 114
References: 39
Citations by year: 2026: 7 · 2025: 14 · 2024: 27 · 2023: 27 · 2022: 20 · 2021: 13 · 2020: 6
FWCI: 6.30
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/EU.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
arXiv (Cornell University)
Funders: Agencia Estatal de Investigación (MDM-2016-0656, TIN2017-90086-R)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
Keywords: deep reinforcement learning, trajectory learning, sparse rewards, state representation, grasping, soft robotics

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Dynamic Cloth Manipulation with Deep Reinforcement Learning

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