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It is desired to equip robots with the capability of interacting with various soft materials as they are ubiquitous in the real world.
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Y. Hu, Y. Fang, Z. Ge, Z. Qu, Y. Zhu, A. Pradhana, and C. Jiang · 2018
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T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
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K. Zhu, X. He, S. Li, H. Wang, and G. Wang · 2019
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E. Alvarado, C. Paliard, D. Rohmer, and M.-P. Cani · 2022
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Fluidlab: A differentiable environment for benchmarking complex fluid manipulation
Z. Xian, B. Zhu, Z. Xu, H.-Y. Tung, A. Torralba, K. Fragkiadaki, and C. Gan · 2023
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Differentiable simulation of soft multi-body systems
Y.-L. Qiao, J. Liang, V. Koltun, and M. C. Lin · 2021
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Diffpd: Differentiable projective dynamics
T. Du, K. Wu, P. Ma, S. Wah, A. Spielberg, D. Rus, and W. Matusik · 2021
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An end-to-end differentiable framework for contact-aware robot design
J. Xu, T. Chen, L. Zlokapa, M. Foshey, W. Matusik, S. Sueda, and P. Agrawal · 2021
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Flingbot: The unreasonable effectiveness of dynamic manipulation for cloth unfolding
H. Ha and S. Song · 2021
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Stable-baselines3: Reliable reinforcement learning implementations
A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann · 2021
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Learning physical dynamics with subequivariant graph neural networks
J. Han, W. Huang, H. Ma, J. Li, J. Tenenbaum, and C. Gan · 2022
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Learning physical dynamics with subequivariant graph neural networks
J. Han, W. Huang, H. Ma, J. Li, J. B. Tenenbaum, and C. Gan · 2022
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Roboninja: Learning an adaptive cutting policy for multi-material objects
Z. Xu, Z. Xian, X. Lin, C. Chi, Z. Huang, C. Gan, and S. Song · 2023
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Daxbench: Benchmarking deformable object manipulation with differentiable physics
S. Chen, Y. Xu, C. Yu, L. Li, X. Ma, Z. Xu, and D. Hsu · 2023
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S. Li, Z. Huang, T. Chen, T. Du, H. Su, J. B. Tenenbaum, and C. Gan · 2023
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Diff-lfd: Contact-aware model-based learning from visual demonstration for robotic manipulation via differentiable physics-based simulation and rendering
X. Zhu, J. Ke, Z. Xu, Z. Sun, B. Bai, J. Lv, Q. Liu, Y. Zeng, Q. Ye, C. Lu, et al · 2023
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Softzoo: A soft robot co-design benchmark for locomotion in diverse environments
T.-H. Wang, P. Ma, A. E. Spielberg, Z. Xian, H. Zhang, J. B. Tenenbaum, D. Rus, and C. Gan · 2023
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Flipbot: Learning continuous paper flipping via coarse-to-fine exteroceptive-proprioceptive exploration
C. Zhao, C. Jiang, J. Cai, M. Y. Wang, H. Yu, and Q. Chen · 2023
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Learning foresightful dense visual affordance for deformable object manipulation
R. Wu, C. Ning, and H. Dong · 2023
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Hierarchical planning for rope manipulation using knot theory and a learned inverse model
M. Sudry, T. Jurgenson, A. Tamar, and E. Karpas · 2023
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Visuotactile affordances for cloth manipulation with local control
N. Sunil, S. Wang, Y. She, E. Adelson, and A. R. Garcia · 2023
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Robocook: Long-horizon elasto-plastic object manipulation with diverse tools
H. Shi, H. Xu, S. Clarke, Y. Li, and J. Wu · 2023
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Manipulation via membranes: High-resolution and highly deformable tactile sensing and control
M. Oller, M. P. i Lisbona, D. Berenson, and N. Fazeli · 2023
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Granular gym: High performance simulation for robotic tasks with granular materials
D. Millard, D. Pastor, J. Bowkett, P. Backes, and G. S. Sukhatme · 2023
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Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds
D. Seita, Y. Wang, S. J. Shetty, E. Y. Li, Z. Erickson, and D. Held · 2023
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Diffvl: Scaling up soft body manipulation using vision-language driven differentiable physics
Z. Huang, F. Chen, Y. Pu, C. Lin, H. Su, and C. Gan · 2024
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