The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
Dynamic Cloth Manipulation with Deep Reinforcement Learning
Open accessAbstract
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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“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 [226]
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“Robust velocity control is still an unsolved challenge in this domain [20,29,226], although few attempt such low-level velocity control to conduct dynamic manipulation [20,29,226,227,228].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [226]
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Cites
Continuous control with deep reinforcement learning
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Overcoming Exploration in Reinforcement Learning with Demonstrations
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Universal Value Function Approximators
Schaul, T., Horgan, D., Gregor, K., & Silver, D. (2015) · Cited by 701
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Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., & Riedmiller, M. (2017) · arXiv · doi:10.48550/arxiv.1707.08817 · Cited by 478
SOFA: A Multi-Model Framework for Interactive Physical Simulation
Fauré, F., Christian Duriez, A., Delingette, H., Allard, J., Gilles, B., Marchesseau, S., Talbot, H., Courtecuisse, H., Bousquet, G., Peterlík, I., et al. (2012) · Studies in mechanobiology, tissue engineering and biomaterials · doi:10.1007/8415_2012_125 · Cited by 475
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- coumans, 2016, Pybullet, a python module for physics simulation for games, robotics and machine learning, Github Repository
Cited by
Sorted by citations
Items 101–113 of 113
A Learning-Based Control Framework for Human-Like Whip Targeting
Wang, J., & Xiong, X. (2024) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob60516.2024.10719935
Air-Drying: An Attempt of Completed Garment Unfolding
Qiu, Y., Yan, W., Liu, J., Zhao, Y., Liu, X., Xu, L., & Cao, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-0798-3_34
Dehaybe, L., & Brüls, O. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5387664
PDDL-Guided Robot Cloth Folding from Simulation to Real World
Dželo, A., Sestic, A., Gams, A., & Krivić, S. (2025) · International Conference on Information, Communication and Automation Technologies (ICAT) · doi:10.1109/icat66432.2025.11189286
A Framework for Digital Twin-based Robotic Cloth Manipulation
Anatoliotakis, N. E., Koustoumpardis, P. Ν., & Moustakas, K. (2025) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-025-02317-1
Sim, H., Liu, J., Delehelle, D., Wu, X., Caldwell, D. G., & Chen, F. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3623331
PolyFold: A Generalizable Framework for Language-Conditioned Bimanual Cloth Folding
Du, H., Xu, K., Xiong, R., & Wang, Y. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3667056
Optimizing decision-making for human-robot collaboration
Daniel, M. (2022) · theses.fr (ABES) · doi:10.70675/c6fa8163zc855z442bzb6d1z78ba24181b4d · Cited by 1
Shape tracking and servoing of deformable objects
Bushehri, S. M. S. (2023) · doi:10.70675/92f0ba7ez3eabz4393za6dcza7485bac7135
Dual-Arm Cloth Unfolding via a Two-Stage Perception and Deep Reinforcement Learning Approach
Zhang, M., Tsoumplekas, G., Spyridis, Y., & Argyriou, V. (2026) · International Conference on Automation, Robotics and Applications · doi:10.1109/icara69401.2026.11480355
KEM-Fabric: A Fabric Manipulation Method Based on Key Manipulation Sequence Matching
Wang, X., Zhu, Y., Yu, F., & Sun, Y. (2026) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac71005.2026.11615514
Automating the creation of fashion patterns using deep learning algorithms
Alsabhi, R. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1828627
Curriculum-Based Reinforcement Learning for Simulated Robotic Basketball Throwing
Osório, D., Amorim, I., Afonseca, P., Leão, G., & Reis, L. P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30156-7_22
Metadata
- 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)
- 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
- 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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