Fetching the paper…
Reading the bibliography…
Multi-task learning of deformable object manipulation is a challenging problem in robot manipulation.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems
2017
Earlier work this paper cites.
A. Nair, D. Chen, P. Agrawal, P. Isola, P. Abbeel, J. Malik, and S. Levine, “Combining self-supervised learning and imitation for vision-based rope manipulation,” in 2017 IEEE international conference on robotics and automation (ICRA)
2017
Earlier work this paper cites.
J. Matas, S. James, and A. J. Davison, “Sim-to-real reinforcement learning for deformable object manipulation,” in Conference on Robot Learning
2018
Earlier work this paper cites.
2020
Earlier work this paper cites.
Y. Wu, W. Yan, T. Kurutach, L. Pinto, and P. Abbeel, “Learning to manipulate deformable objects without demonstrations,” in Robotics: Science and Systems
2020
Earlier work this paper cites.
H. Bertiche, M. Madadi, and S. Escalera, “Cloth3d: clothed 3d humans,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XX 16
2020
Earlier work this paper cites.
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 2021 IEEE International Conference on Robotics and Automation (ICRA)
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 Conference on Robot Learning
2021
Earlier work this paper cites.
S. Zimmermann, R. Poranne, and S. Coros, “Dynamic manipulation of deformable objects with implicit integration,” IEEE Robotics and Automation Letters
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al
2021
Earlier work this paper cites.
W. Kim, B. Son, and I. Kim, “Vilt: Vision-and-language transformer without convolution or region supervision,” in International Conference on Machine Learning
2021
Cited alongside, same era.
W. Yan, A. Vangipuram, P. Abbeel, and L. Pinto, “Learning predictive representations for deformable objects using contrastive estimation,” in Conference on Robot Learning
2021
Cited alongside, same era.
W. Kim, B. Son, and I. Kim, “Vilt: Vision-and-language transformer without convolution or region supervision,” in International Conference on Machine Learning
2021
Cited alongside, same era.
Z. Wu, P. Jain, M. Wright, A. Mirhoseini, J. E. Gonzalez, and I. Stoica, “Representing long-range context for graph neural networks with global attention,” Advances in Neural Information Processing Systems
2021
Cited alongside, same era.
X. Lin, Y. Wang, Z. Huang, and D. Held, “Learning visible connectivity dynamics for cloth smoothing,” in Conference on Robot Learning
2022
Later among the works it cites.
X. Ma, D. Hsu, and W. S. Lee, “Learning latent graph dynamics for visual manipulation of deformable objects,” in 2022 International Conference on Robotics and Automation (ICRA)
2022
Later among the works it cites.
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 Conference on Robot Learning
2022
Later among the works it cites.
2022
Later among the works it cites.
S. Nair, E. Mitchell, K. Chen, S. Savarese, C. Finn, et al
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
B. Thach, B. Y. Cho, A. Kuntz, and T. Hermans, “learning visual shape control of novel 3d deformable objects from partial-view point clouds,” in 2022 International Conference on Robotics and Automation (ICRA)
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 Conference on Robot Learning
2022
Cited alongside, same era.
M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in Conference on Robot Learning
2022
Cited alongside, same era.
A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian, et al
2022
Cited alongside, same era.
J. Zhu, A. Cherubini, C. Dune, D. Navarro-Alarcon, F. Alambeigi, D. Berenson, F. Ficuciello, K. Harada, J. Kober, X. Li, et al
2022
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
O. Mees, L. Hermann, E. Rosete-Beas, and W. Burgard, “Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,” IEEE Robotics and Automation Letters
2022
Later among the works it cites.
S. Nair, E. Mitchell, K. Chen, S. Savarese, C. Finn, et al
2022
Later among the works it cites.
K. Mo, C. Xia, X. Wang, Y. Deng, X. Gao, and B. Liang, “Foldsformer: Learning sequential multi-step cloth manipulation with space-time attention,” IEEE Robotics and Automation Letters
2023
Closest in time.