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 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 [226]
The Introduction (p. 2) and Results (p. 5) describe using a common deep-RL approach with goal states across three cloth tasks, including folding. They do not study cloth flattening or show that flattening and folding were trained jointly; the paper also does not establish that those tasks have identical underlying dynamics or similar representations. (LLM, conf=0.86)
Experimental — accuracy not yet measuredRobust velocity control is still an unsolved challenge in this domain, although few attempt such low-level velocity control to conduct dynamic manipulation.
“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]
The Introduction (p. 1) says most prior deformable-object work considered static manipulation, and State and Action (p. 4) says this study's policy specifies target manipulator velocities for dynamic cloth tasks. These passages support the claim that few approaches attempt this kind of velocity-based dynamic manipulation, but the paper does not state that robust velocity control remains unsolved. (LLM, conf=0.83)
Experimental — accuracy not yet measured
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ShakingBot: dynamic manipulation for bagging
Gu, N., Zhang, Z., He, R., & Yu, L. (2024) · Robotica · doi:10.1017/s0263574723001753 · Cited by 10 · see citing works
Demonstrating Cloth Folding to Robots: Design and Evaluation of a 2D and a 3D User Interface
Waymouth, B., Cosgun, A., Newbury, R., Tran, T. T., Chan, W. P., Drummond, T. W., & Croft, E. A. (2021) · IEEE RO-MAN · doi:10.1109/ro-man50785.2021.9515469 · Cited by 11 · see citing works
Bridging language and action: A survey of language-conditioned robot manipulation
Yao, X., Zhou, H., Mees, O., Meng, Y., Xiao, T. G., Bisk, Y., Oh, J., Johns, E., Shridhar, M., Shah, D., et al. (2026) · The International Journal of Robotics Research · doi:10.1177/02783649261468360 · Cited by 23 · see citing works
Deformable Object Manipulation With Constraints Using Path Set Planning and Tracking
Huang, J., Chu, X., Ma, X., & Au, K. W. S. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3306618 · Cited by 9 · see citing works
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning
Niu, Y., Jin, S., Zhang, Z., Zhu, J., Zhao, D. X., & Zhang, L. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342221 · Cited by 9 · see citing works
SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation
Bartsch, A., Avra, C., & Farimani, A. B. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610899 · Cited by 9 · see citing works
Zhang, Y., Liu, F., Xiao, L., & Yip, M. C. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611111 · Cited by 7 · see citing works
Robotic Fabric Flattening with Wrinkle Direction Detection
Qiu, Y., Zhu, J., Santina, C. D., Gienger, M., & Kober, J. (2024) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-63596-0_30 · Cited by 7 · see citing works
Dynamic manipulation of unknown string by robot arm: realizing momentary string shapes
Tabata, K., Seki, H., Tsuji, T., Hiramitsu, T., & Hikizu, M. (2020) · ROBOMECH Journal · doi:10.1186/s40648-020-00187-w · Cited by 7 · see citing works
Topological representation of cloth state for robot manipulation
Strazzeri, F., & Carme Torras, A. (2021) · Autonomous Robots · doi:10.1007/s10514-021-09968-7 · Cited by 7 · see citing works
A robotic skill transfer learning framework of dynamic manipulation for fabric placement
Fu, T., Li, C., Bai, Y., Li, F., Wu, J., Wang, C., & Song, R. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104216 · Cited by 5 · see citing works
Wang, H., Dong, Z., Zhu, T., Lei, H., Shi, W., Zhang, Z., Luo, W., Wan, W., Chen, X., & Huang, J. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3627775 · Cited by 5 · see citing works
Garment Diffusion Models for Robot-Assisted Dressing
Kotsovolis, S., & Demiris, Y. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3518104 · Cited by 5 · see citing works
Soft-body dynamics induces energy efficiency in undulatory swimming: A deep learning study
Li, G., Shintake, J., & Hayashibe, M. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1102854 · Cited by 5 · see citing works
Besselaar, L., & Santina, C. D. (2022) · IEEE International Conference on Soft Robotics (RoboSoft) · doi:10.1109/robosoft54090.2022.9762089 · Cited by 5 · see citing works
Borràs, J., Boix-Granell, A., Foix, S., & Carme Torras, A. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161122 · Cited by 5 · see citing works
Closing the Sim2Real Gap in Dynamic Cloth Manipulation.Preprint
Hietala, J., Blanco–Mulero, D., Alcan, G., & Kyrki, V. (2021) · arXiv (Cornell University) · Cited by 5 · see citing works
A Brief Review of Recent Hierarchical Reinforcement Learning for Robotic Manipulation
Liu, S., Tang, T., Zhang, W., Cui, J., & Xu, X. (2022) · International Conference on Information Systems and Computing Technology (ISCTech) · doi:10.1109/isctech58360.2022.00119 · Cited by 4 · see citing works
Manipulator Control using Federated Deep Reinforcement Learning
Shivkumar, S., & Kumaar, A. A. N. (2024) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct62155.2024.10677205 · Cited by 4 · see citing works
Efficient Planar Fabric Repositioning: Deformation-Aware RRT* for Non-Prehensile Fabric Manipulation
Wang, Y., Yang, L., Zhou, P., Qi, J., Lu, L., Zhu, J., & Pan, J. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3484131 · Cited by 4 · see citing works
Effective cloth folding trajectories in simulation with only two parameters
Gusseme, V.-L. D., & wyffels, F. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.989702 · Cited by 5 · see citing works
Arnold, S., Tanaka, D., & Yamazaki, K. (2023) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.1045747 · Cited by 6 · see citing works
You, Y., & Ji, Z. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103025 · Cited by 3 · see citing works
Islam, S., Owen, C. B., Mukherjee, R., & Woodring, I. (2024) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3652037.3652067 · Cited by 3 · see citing works
A Parallelized Iterative Algorithm for Real-Time Simulation of Long Flexible Cable Manipulation
Lee, J., Lee, M., Yoon, J., & Lee, D. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561905 · Cited by 3 · see citing works
Towards Zero-Shot Cross-Agent Transfer Learning Via Latent-Space Universal Notice Network
Beaussant, S., Lengagne, S., Thuilot, B., & Stasse, O. (2024) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2024.104862 · Cited by 2 · see citing works
An offline reinforcement learning-based framework for proactive robot assistance in assembly task
You, Y., Cai, B., & Ji, Z. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111313 · Cited by 2 · see citing works
Qian, H., Zhang, H., Shao, J., Zhang, J., Gu, J. J., Song, W., & Zhu, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611076 · Cited by 2 · see citing works
Chen, J., Ning, G., Ma, L., & Liao, H. (2025) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2025.3560399 · Cited by 2 · see citing works
Learning from the Tangram to Solve Mini Visual Tasks
Zhao, Y., Qiu, L., Lu, P., Shi, F., Han, T., & Zhu, S. C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20260 · Cited by 1 · see citing works
Topological reconstruction of sampled surfaces via Morse theory
Coltraro, F., Amorós, J., Alberich‐Carramiñana, M., & Carme Torras, A. (2025) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2025.116872 · Cited by 1 · see citing works
Liu, J., Yang, S., Wang, Y., Zhao, H., & Ding, H. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127676 · Cited by 1 · see citing works
Robotic Paper Wrapping by Learning Force Control
Hanai, H., Kiyokawa, T., Wan, W., & Harada, K. (2025) · IEEE Access · doi:10.1109/access.2025.3606495 · Cited by 1 · see citing works
One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes
Jia, W., Yang, F., Duan, M., Chen, X., Wang, Y., Jiang, Y., Chen, W., Yang, K., & Li, Z. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246431 · Cited by 1 · see citing works
Dynamic Bimanual Cloth Manipulation via Dynamic Movement Primitives and Reinforcement Learning
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Quadratic Dynamic Matrix Control for Fast Cloth Manipulation
Caldarelli, E., Colomé, A., Ocampo‐Martínez, C., & Carme Torras, A. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341850 · Cited by 1 · see citing works
Ricci Planner: Zero-Shot Transfer for Goal-Conditioned Reinforcement Learning via Geometric Flow
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Learning to bag with a simulation‐free reinforcement learning framework for robots
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Object Manipulation in Marine Environments using Reinforcement Learning
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A Representation of Cloth States based on a Derivative of the Gauss Linking Integral
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Robotic Manipulation of Deformable Objects Using DP-Resnet-Transporter NetworksPreprint
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Sample-Efficient Multimodal Dynamics Modeling for Risk-Sensitive Reinforcement Learning
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Learning robotic manipulation of deformable objects in the real world
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Semantic State Prediction in Robotic Cloth Manipulation
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Deformable Object Manipulation Using Human Demonstration Enhanced Deep Deterministic Policy Gradient
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A soft actor-critic based method for two manipulators coordinated lifting and balancing
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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)
- 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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