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Realistic physics engines play a crucial role for learning to manipulate deformable objects such as garments in simulation.
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2020
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2020
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H. Ha and S. Song, “Flingbot: The unreasonable effectiveness of dynamic manipulation for cloth unfolding,” in Conference on Robot Learning (CoRL) , 2021
2021
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2021
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D. Seita, P. Florence, J. Tompson, E. Coumans, V. Sindhwani, K. Goldberg, and A. Zeng, “Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks,” in IEEE International Conference on Robotics and Automation , 2021
2021
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2021
Cited alongside, same era.
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2021
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2022
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2022
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2022
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V. Lim, H. Huang, L. Y. Chen, J. Wang, J. Ichnowski, D. Seita, M. Laskey, and K. Goldberg, “Real2sim2real: Self-supervised learning of physical single-step dynamic actions for planar robot casting,” in IEEE International Conference on Robotics and Automation , 2022
2022
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2022
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2023
Closest in time.
S. Chen, Y. Xu, C. Yu, L. Li, X. Ma, Z. Xu, and D. Hsu, “Daxbench: Benchmarking deformable object manipulation with differentiable physics,” in The 11th International Conference on Learning Representations , 2023
2023
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R. Proesmans, A. Verleysen, and F. Wyffels, “Unfoldir: Tactile robotic unfolding of cloth,” IEEE Robotics and Automation Letters , vol. 8, no. 8, pp. 4426–4432, 2023
2023
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Z. Huang, X. Lin, and D. Held, “Self-supervised cloth reconstruction via action-conditioned cloth tracking,” in IEEE International Conference on Robotics and Automation , 2023
2023
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R. Garnett, Bayesian optimization . Cambridge University Press, 2023
2023
Closest in time.