Fetching the paper…
Reading the bibliography…
Pottery creation is a complicated art form that requires dexterous, precise and delicate actions to slowly morph a block of clay to a meaningful, and often useful 3D goal shape.
Y. Rubner, C. Tomasi, and L. J. Guibas, “The earth mover’s distance as a metric for image retrieval,” International journal of computer vision , vol. 40, pp. 99–121, 2000
2000
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
2017
Earlier work this paper cites.
H. Fan, H. Su, and L. J. Guibas, “A point set generation network for 3d object reconstruction from a single image,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 605–613
2017
Earlier work this paper cites.
C. Qi, X. Lin, and D. Held, “Learning closed-loop dough manipulation using a differentiable reset module,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 9857–9864, 2022
2022
Earlier work this paper cites.
X. Lin, C. Qi, Y. Zhang, Z. Huang, K. Fragkiadaki, Y. Li, C. Gan, and D. Held, “Planning with spatial-temporal abstraction from point clouds for deformable object manipulation,” CoRL , 2022
2022
Earlier work this paper cites.
J. Ondras, D. Ni, X. Deng, Z. Gu, H. Zheng, and T. Bhattacharjee, “Robotic dough shaping,” in 2022 22nd International Conference on Control, Automation and Systems (ICCAS) . IEEE, 2022, pp. 300–307
2022
Earlier work this paper cites.
J.-T. Kim, F. Ruggiero, V. Lippiello, and B. Siciliano, “Planning framework for robotic pizza dough stretching with a rolling pin,” in Robot dynamic manipulation: perception of deformable objects and nonprehensile manipulation control . Springer, 2022, pp. 229–253
2022
Earlier work this paper cites.
X. Yu, L. Tang, Y. Rao, T. Huang, J. Zhou, and J. Lu, “Point-bert: Pre-training 3d point cloud transformers with masked point modeling,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 19 313–19 322
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
C. Chi, Z. Xu, S. Feng, E. Cousineau, Y. Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” The International Journal of Robotics Research , p. 02783649241273668, 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
H. Zhu, Y. Wang, D. Huang, W. Ye, W. Ouyang, and T. He, “Point cloud matters: Rethinking the impact of different observation spaces on robot learning,” Advances in Neural Information Processing Systems , vol. 37, pp. 77 799–77 830, 2024
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Later among the works it cites.
D. Bauer, Z. Xu, and S. Song, “Doughnet: A visual predictive model for topological manipulation of deformable objects,” in European Conference on Computer Vision . Springer, 2024, pp. 92–108
2024
Later among the works it cites.
2024
Later among the works it cites.
U. Yoo, A. Hung, J. Francis, J. Oh, and J. Ichnowski, “Ropotter: Toward robotic pottery and deformable object manipulation with structural priors,” in 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids) . IEEE, 2024, pp. 843–850
2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2024
Cited alongside, same era.
S. Zhaole, J. Zhu, and R. B. Fisher, “Dexdlo: Learning goal-conditioned dexterous policy for dynamic manipulation of deformable linear objects,” in 2024 IEEE international conference on robotics and automation (ICRA) . IEEE, 2024, pp. 16 009–16 015
2024
Cited alongside, same era.
P. Jamdagni and Y.-B. Jia, “Robotic cutting of fruits and vegetables: Modeling the effects of deformation, fracture toughness, knife edge geometry, and motion,” IEEE Transactions on Robotics , 2024
2024
Cited alongside, same era.
H. Shi, H. Xu, Z. Huang, Y. Li, and J. Wu, “Robocraft: Learning to see, simulate, and shape elasto-plastic objects in 3d with graph networks,” The International Journal of Robotics Research , vol. 43, no. 4, pp. 533–549, 2024
2024
Cited alongside, same era.
A. Bartsch, C. Avra, and A. B. Farimani, “Sculptbot: Pre-trained models for 3d deformable object manipulation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 12 548–12 555
2024
Cited alongside, same era.
A. Bartsch, A. Car, C. Avra, and A. B. Farimani, “Sculptdiff: Learning robotic clay sculpting from humans with goal conditioned diffusion policy,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 7307–7314
2024
Cited alongside, same era.
C. Zhou, H. Xu, J. Hu, F. Luan, Z. Wang, Y. Dong, Y. Zhou, and B. He, “Ssfold: Learning to fold arbitrary crumpled cloth using graph dynamics from human demonstration,” IEEE Transactions on Automation Science and Engineering , 2025
2025
Closest in time.
2025
Closest in time.
Z. Zhang, X. Chu, Y. Tang, L. Zhao, J. Huang, Z. Jiang, and K. S. Au, “Manipulating elasto-plastic objects with 3d occupancy and learning-based predictive control,” IEEE Robotics and Automation Letters , 2025
2025
Closest in time.
Y. You, B. Shen, C. Deng, H. Geng, S. Wei, H. Wang, and L. Guibas, “Make a donut: Hierarchical emd-space planning for zero-shot deformable manipulation with tools,” IEEE Robotics and Automation Letters , 2025
2025
Closest in time.
A. Bartsch and A. B. Farimani, “Planning and reasoning with 3d deformable objects for hierarchical text-to-3d robotic shaping,” IEEE Robotics and Automation Letters , pp. 1–8, 2025
2025
Closest in time.