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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In the citing paper
“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
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
MuJoCo: A physics engine for model-based control
Todorov, E., Erez, T., & Tassa, Y. (2012) · 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2012.6386109 · Cited by 4,767
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Gu, S., Holly, E., Lillicrap, T., & Levine, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989385 · Cited by 1,582
Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Abbeel, P., & Zaremba, W. (2017) · arXiv · doi:10.48550/arxiv.1707.01495 · Cited by 1,355
Reinforcement learning of motor skills with policy gradients
Peters, J., & Schaal, S. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.02.003 · Cited by 831
Overcoming Exploration in Reinforcement Learning with Demonstrations
Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8463162 · Cited by 712
Universal Value Function Approximators
Schaul, T., Horgan, D., Gregor, K., & Silver, D. (2015) · Cited by 701
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016) · arXiv · doi:10.48550/arxiv.1606.01540 · Cited by 582
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
Research works printed but not linked: 2
- 10.1109/IROS.2011.6048873 · DOI
- coumans, 2016, Pybullet, a python module for physics simulation for games, robotics and machine learning, Github Repository
Cited by
Sorted by citations
A review on reinforcement learning for contact-rich robotic manipulation tasks
Elguea-Aguinaco, Í., Serrano-Muñoz, A., Chrysostomou, D., Inziarte-Hidalgo, I., Bøgh, S., & Arana-Arexolaleiba, N. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102517 · Cited by 177
Seita, D., Florence, P., Tompson, J., Coumans, E., Sindhwani, V., Goldberg, K., & Zeng, A. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561391 · Cited by 130
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
Seita, D., Ganapathi, A., Hoque, R., Hwang, M., Cen, E., Tanwani, A. K., Balakrishna, A., Thananjeyan, B., Ichnowski, J., Jamali, N., et al. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341608 · Cited by 113
Mohammed, M. Q., Kwek, L. C., & Chua, S. C. (2020) · IEEE Access · doi:10.1109/access.2020.3027923 · Cited by 111
FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding
Ha, H., & Song, S. (2021) · arXiv · doi:10.48550/arxiv.2105.03655 · Cited by 93
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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