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Garment folding is a common yet challenging task in robotic manipulation.
S. Tellex, T. Kollar, S. Dickerson, M. Walter, A. Banerjee, S. Teller, and N. Roy, “Understanding natural language commands for robotic navigation and mobile manipulation,” in AAAI , 2011
2011
Earlier work this paper cites.
J. Stria, D. Prǔša, V. Hlaváč, L. Wagner, V. Petrik, P. Krsek, and V. Smutnỳ, “Garment perception and its folding using a dual-arm robot,” in IROS . IEEE, 2014, pp. 61–67
2014
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” NIPS , 2015
2015
Earlier work this paper cites.
D. K. Misra, J. Sung, K. Lee, and A. Saxena, “Tell me dave: Context-sensitive grounding of natural language to manipulation instructions,” IJRR , vol. 35, no. 1-3, pp. 281–300, 2016
2016
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
D. Tanaka, S. Arnold, and K. Yamazaki, “Emd net: An encode–manipulate–decode network for cloth manipulation,” RAL , 2018
2018
Earlier work this paper cites.
J. Hatori, Y. Kikuchi, S. Kobayashi, K. Takahashi, Y. Tsuboi, Y. Unno, W. Ko, and J. Tan, “Interactively picking real-world objects with unconstrained spoken language instructions,” in ICRA . IEEE, 2018
2018
Earlier work this paper cites.
J. Liang, V. Makoviychuk, A. Handa, N. Chentanez, M. Macklin, and D. Fox, “Gpu-accelerated robotic simulation for distributed reinforcement learning,” in Conference on Robot Learning . PMLR, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
C. Paxton, Y. Bisk, J. Thomason, A. Byravan, and D. Foxl, “Prospection: Interpretable plans from language by predicting the future,” in ICRA . IEEE, 2019, pp. 6942–6948
2019
Earlier work this paper cites.
H. Bertiche, M. Madadi, and S. Escalera, “Cloth3d: clothed 3d humans,” in ECCV . Springer, 2020, pp. 344–359
2020
Earlier work this paper cites.
R. Shi, Z. Xue, Y. You, and C. Lu, “Skeleton merger: an unsupervised aligned keypoint detector,” in CVPR , 2021, pp. 43–52
2021
Earlier work this paper cites.
A. Ganapathi, P. Sundaresan, B. Thananjeyan, A. Balakrishna, D. Seita, J. Grannen, et al. , “Learning dense visual correspondences in simulation to smooth and fold real fabrics,” in ICRA , 2021
2021
Earlier work this paper cites.
R. Lee, D. Ward, V. Dasagi, A. Cosgun, J. Leitner, and P. Corke, “Learning arbitrary-goal fabric folding with one hour of real robot experience,” in CoRL . PMLR, 2021, pp. 2317–2327
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
J. Zhu, A. Cherubini, C. Dune, D. Navarro-Alarcon, F. Alambeigi, D. Berenson, et al. , “Challenges and outlook in robotic manipulation of deformable objects,” IEEE Robotics & Automation Magazine , 2022
2022
Earlier work this paper cites.
J. Hietala, D. Blanco-Mulero, G. Alcan, and V. Kyrki, “Learning visual feedback control for dynamic cloth folding,” in IROS . IEEE, 2022
2022
Earlier work this paper cites.
R. Hoque, K. Shivakumar, S. Aeron, G. Deza, A. Ganapathi, A. Wong, J. Lee, A. Zeng, V. Vanhoucke, and K. Goldberg, “Learning to fold real garments with one arm: A case study in cloud-based robotics research,” in IROS , 2022
2022
Cited alongside, same era.
Y. Avigal, L. Berscheid, T. Asfour, T. Kröger, and K. Goldberg, “Speedfolding: Learning efficient bimanual folding of garments,” in IROS . IEEE, 2022, pp. 1–8
2022
Cited alongside, same era.
T. Weng, S. M. Bajracharya, Y. Wang, K. Agrawal, and D. Held, “Fabricflownet: Bimanual cloth manipulation with a flow-based policy,” in CoRL . PMLR, 2022, pp. 192–202
2022
Cited alongside, same era.
X. Lin, Y. Wang, Z. Huang, and D. Held, “Learning visible connectivity dynamics for cloth smoothing,” in CoRL , 2022, pp. 256–266
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
B. Zhou, H. Zhou, T. Liang, Q. Yu, S. Zhao, Y. Zeng, J. Lv, S. Luo, Q. Wang, X. Yu, H. Chen, C. Lu, and L. Shao, “Clothesnet: An information-rich 3d garment model repository with simulated clothes environment,” in ICCV , October 2023, pp. 20 428–20 438
2023
Later among the works it cites.
X. Yu, S. Zhao, S. Luo, G. Yang, and L. Shao, “Diffclothai: Differentiable cloth simulation with intersection-free frictional contact and differentiable two-way coupling with articulated rigid bodies,” in IROS , 2023, pp. 400–407
2023
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2022
Cited alongside, same era.
E. Stengel-Eskin, A. Hundt, Z. He, A. Murali, N. Gopalan, M. Gombolay, and G. Hager, “Guiding multi-step rearrangement tasks with natural language instructions,” in CoRL , 2022, pp. 1486–1501
2022
Cited alongside, same era.
M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in CoRL , 2022, pp. 894–906
2022
Cited alongside, same era.
S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta, “R3m: A universal visual representation for robot manipulation,” CoRL , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Cited alongside, same era.
A. Canberk, C. Chi, H. Ha, B. Burchfiel, E. Cousineau, S. Feng, and S. Song, “Cloth funnels: Canonicalized-alignment for multi-purpose garment manipulation,” in ICRA . IEEE, 2023, pp. 5872–5879
2023
Cited alongside, same era.
Later among the works it cites.
2023
Later among the works it cites.
R. Wu, H. Lu, Y. Wang, Y. Wang, and H. Dong, “Unigarmentmanip: A unified framework for category-level garment manipulation via dense visual correspondence,” in CVPR , 2024, pp. 16 340–16 350
2024
Later among the works it cites.
C. He, L. Meng, Z. Sun, J. Wang, and M. Q.-H. Meng, “Fabricfolding: learning efficient fabric folding without expert demonstrations,” Robotica , vol. 42, no. 4, pp. 1281–1296, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Xu, C. Gao, Z. Liu, G. Yang, C. Tie, H. Zheng, H. Zhou, et al. , “Manifoundation model for general-purpose robotic manipulation of contact synthesis with arbitrary objects and robots,” in IROS , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Deng, K. Mo, C. Xia, and X. Wang, “Learning language-conditioned deformable object manipulation with graph dynamics,” in ICRA . IEEE, 2024, pp. 7508–7514
2024
Later among the works it cites.
C. Gao, H. Zhang, Z. Xu, C. Zhehao, and L. Shao, “Flip: Flow-centric generative planning for general-purpose manipulation tasks,” in CoRL Workshop on Learning Effective Abstractions for Planning , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Ze, G. Zhang, K. Zhang, C. Hu, M. Wang, and H. Xu, “3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations,” in RSS , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.