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If a robot masters folding a kitchen towel, we would expect it to master folding a large beach towel.
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C. Deng, O. Litany, Y. Duan, A. Poulenard, A. Tagliasacchi, and L. J. Guibas, “Vector neurons: A general framework for so (3)-equivariant networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 12 200–12 209
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2021
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C. Chi and S. Song, “Garmentnets: Category-level pose estimation for garments via canonical space shape completion,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3324–3333
2021
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W. Sun, A. Tagliasacchi, B. Deng, S. Sabour, S. Yazdani, G. E. Hinton, and K. M. Yi, “Canonical capsules: Self-supervised capsules in canonical pose,” Advances in Neural information processing systems , vol. 34, pp. 24 993–25 005, 2021
2021
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A. Simeonov, Y. Du, A. Tagliasacchi, J. B. Tenenbaum, A. Rodriguez, P. Agrawal, and V. Sitzmann, “Neural descriptor fields: Se (3)-equivariant object representations for manipulation,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6394–6400
2022
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2022
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2022
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O. Katzir, D. Lischinski, and D. Cohen-Or, “Shape-pose disentanglement using se (3)-equivariant vector neurons,” in European Conference on Computer Vision . Springer, 2022, pp. 468–484
2022
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J. Li, S. Luo, C. Deng, C. Cheng, J. Guan, L. Guibas, J. Ma, and J. Peng, “Orientation-aware graph neural networks for protein structure representation learning,” 2022
2022
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H.-X. Yu, J. Wu, and L. Yi, “Rotationally equivariant 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 1456–1464
2022
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2023
Closest in time.
J. Lei, C. Deng, K. Schmeckpeper, L. Guibas, and K. Daniilidis, “Efem: Equivariant neural field expectation maximization for 3d object segmentation without scene supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4902–4912
2023
Closest in time.
A. Simeonov, Y. Du, Y.-C. Lin, A. R. Garcia, L. P. Kaelbling, T. Lozano-Pérez, and P. Agrawal, “Se (3)-equivariant relational rearrangement with neural descriptor fields,” in Conference on Robot Learning . PMLR, 2023, pp. 835–846
2023
Closest in time.
Z. Xue, Z. Yuan, J. Wang, X. Wang, Y. Gao, and H. Xu, “Useek: Unsupervised se (3)-equivariant 3d keypoints for generalizable manipulation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 1715–1722
2023
Closest in time.
T. Weng, D. Held, F. Meier, and M. Mukadam, “Neural grasp distance fields for robot manipulation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 1814–1821
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
M. Jia, D. Wang, G. Su, D. Klee, X. Zhu, R. Walters, and R. Platt, “Seil: Simulation-augmented equivariant imitation learning,” ICRA , 2023
2023
Closest in time.