ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching · Around

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ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching · Around

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S. Calinon, “A tutorial on task-parameterized movement learning and retrieval,” Intelligent service robotics , vol. 9, pp. 1–29, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition . IEEE Computer Society, 2016
2016
Earlier work this paper cites.
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 , vol. 30, 2017
2017
Earlier work this paper cites.
R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
J. Sola, J. Deray, and D. Atchuthan, “A micro lie theory for state estimation in robotics,” arXiv preprint arXiv:1812.01537 , 2018
Original
2018
Earlier work this paper cites.
C. R. Dreher, M. Wächter, and T. Asfour, “Learning object-action relations from bimanual human demonstration using graph networks,” IEEE Robotics and Automation Letters , vol. 5, no. 1, 2019
2019
Earlier work this paper cites.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” arXiv preprint arXiv:2011.13456 , 2020
Original
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
A. Zeng, P. Florence, J. Tompson, S. Welker, J. Chien, M. Attarian, T. Armstrong, I. Krasin, D. Duong, V. Sindhwani et al. , “Transporter networks: Rearranging the visual world for robotic manipulation,” in Conference on Robot Learning , 2021
2021
Earlier work this paper cites.
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko et al. , “Highly accurate protein structure prediction with alphafold,” Nature , vol. 596, no. 7873, pp. 583–589, 2021
2021
Earlier work this paper cites.
A. Liutkus, O. Cıfka, S.-L. Wu, U. Simsekli, Y.-H. Yang, and G. Richard, “Relative positional encoding for transformers with linear complexity,” in International Conference on Machine Learning , 2021
2021
Earlier work this paper cites.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in 9th International Conference on Learning Representations, ICLR , 2021
2021
Earlier work this paper cites.
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
2021
Earlier work this paper cites.
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu et al. , “Rt-1: Robotics transformer for real-world control at scale,” arXiv preprint arXiv:2212.06817 , 2022
Original
2022
Earlier work this paper cites.
S. James, K. Wada, T. Laidlow, and A. J. Davison, “Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022
2022
Earlier work this paper cites.
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 International Conference on Robotics and Automation (ICRA) . IEEE, 2022
2022
Earlier work this paper cites.
Y. Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le, “Flow matching for generative modeling,” arXiv preprint arXiv:2210.02747 , 2022
Original
2022
Earlier work this paper cites.
X. Liu, C. Gong, and Q. Liu, “Flow straight and fast: Learning to generate and transfer data with rectified flow,” arXiv preprint arXiv:2209.03003 , 2022
Original
2022
Earlier work this paper cites.
A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín, “What matters in learning from offline human demonstrations for robot manipulation,” in Conference on Robot Learning , 2022
2022
Earlier work this paper cites.

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2023
Later among the works it cites.

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