Logic
120 lines
5.4 KiB
Plaintext
120 lines
5.4 KiB
Plaintext
\begin{thebibliography}{17}
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\providecommand{\natexlab}[1]{#1}
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\providecommand{\url}[1]{\texttt{#1}}
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\expandafter\ifx\csname urlstyle\endcsname\relax
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\providecommand{\doi}[1]{doi: #1}\else
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\providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi
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\bibitem[Black et~al.(2024)Black, Brown, Driess, Esmail, Equi, Finn, Fusai,
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Groom, Hausman, Ichter, et~al.]{black2024pi0}
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K.~Black, N.~Brown, D.~Driess, A.~Esmail, M.~Equi, C.~Finn, N.~Fusai, L.~Groom,
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K.~Hausman, B.~Ichter, et~al.
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\newblock {$\pi_0$}: A vision-language-action flow model for general robot
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control.
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\newblock \emph{arXiv preprint arXiv:2410.24164}, 2024.
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\bibitem[Chi et~al.(2023)Chi, Xu, Feng, Cousineau, Du, Burchfiel, Tedrake, and
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Song]{chi2023diffusion}
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C.~Chi, Z.~Xu, S.~Feng, E.~Cousineau, Y.~Du, B.~Burchfiel, R.~Tedrake, and
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S.~Song.
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\newblock Diffusion policy: Visuomotor policy learning via action diffusion.
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\newblock \emph{arXiv preprint arXiv:2303.04137}, 2023.
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\bibitem[Ho et~al.(2020)Ho, Jain, and Abbeel]{ho2020denoising}
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J.~Ho, A.~Jain, and P.~Abbeel.
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\newblock Denoising diffusion probabilistic models.
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\newblock In \emph{Advances in Neural Information Processing Systems}, 2020.
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\bibitem[Peebles and Xie(2023)]{peebles2023scalable}
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W.~Peebles and S.~Xie.
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\newblock Scalable diffusion models with transformers.
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\newblock In \emph{IEEE/CVF International Conference on Computer Vision}, 2023.
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\bibitem[Lipman et~al.(2023)Lipman, Chen, Ben-Hamu, Nickel, and
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Le]{lipman2023flow}
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Y.~Lipman, R.~T.~Q. Chen, H.~Ben-Hamu, M.~Nickel, and M.~Le.
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\newblock Flow matching for generative modeling.
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\newblock In \emph{International Conference on Learning Representations}, 2023.
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\bibitem[Liu et~al.(2022)Liu, Gong, and Liu]{liu2022flow}
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X.~Liu, C.~Gong, and Q.~Liu.
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\newblock Flow straight and fast: Learning to generate and transfer data with
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rectified flow.
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\newblock \emph{arXiv preprint arXiv:2209.03003}, 2022.
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\bibitem[Geng et~al.(2025{\natexlab{a}})Geng, Deng, Bai, Kolter, and
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He]{geng2025mean}
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Z.~Geng, M.~Deng, X.~Bai, J.~Z. Kolter, and K.~He.
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\newblock Mean flows for one-step generative modeling.
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\newblock \emph{arXiv preprint arXiv:2505.13447}, 2025{\natexlab{a}}.
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\bibitem[Geng et~al.(2025{\natexlab{b}})Geng, Lu, Wu, Shechtman, Kolter, and
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He]{geng2025improved}
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Z.~Geng, Y.~Lu, Z.~Wu, E.~Shechtman, J.~Z. Kolter, and K.~He.
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\newblock Improved mean flows: On the challenges of fastforward generative
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models.
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\newblock \emph{arXiv preprint arXiv:2512.02012}, 2025{\natexlab{b}}.
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\bibitem[He et~al.(2016)He, Zhang, Ren, and Sun]{he2016deep}
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K.~He, X.~Zhang, S.~Ren, and J.~Sun.
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\newblock Deep residual learning for image recognition.
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\newblock In \emph{IEEE Conference on Computer Vision and Pattern Recognition},
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2016.
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\bibitem[Vaswani et~al.(2017)Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez,
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Kaiser, and Polosukhin]{vaswani2017attention}
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A.~Vaswani, N.~Shazeer, N.~Parmar, J.~Uszkoreit, L.~Jones, A.~N. Gomez,
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L.~Kaiser, and I.~Polosukhin.
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\newblock Attention is all you need.
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\newblock In \emph{Advances in Neural Information Processing Systems}, 2017.
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\bibitem[{Kimi Team}(2026)]{kimi2026attention}
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{Kimi Team}.
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\newblock Attention residuals.
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\newblock \emph{arXiv preprint arXiv:2603.15031}, 2026.
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\bibitem[Zhao et~al.(2023)Zhao, Kumar, Levine, and Finn]{zhao2023learning}
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T.~Z. Zhao, V.~Kumar, S.~Levine, and C.~Finn.
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\newblock Learning fine-grained bimanual manipulation with low-cost hardware.
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\newblock \emph{arXiv preprint arXiv:2304.13705}, 2023.
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\bibitem[Shukor et~al.(2025)Shukor, Aubakirova, Capuano, Kooijmans, Palma,
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Zouitine, Aractingi, Pascal, Russi, Marafioti, et~al.]{shukor2025smolvla}
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M.~Shukor, D.~Aubakirova, F.~Capuano, P.~Kooijmans, S.~Palma, A.~Zouitine,
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M.~Aractingi, C.~Pascal, M.~Russi, A.~Marafioti, et~al.
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\newblock Smolvla: A vision-language-action model for affordable and efficient
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robotics.
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\newblock \emph{arXiv preprint arXiv:2506.01844}, 2025.
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\bibitem[Brohan et~al.(2022)Brohan, Brown, Carbajal, Chebotar, Dabis, Finn,
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Gopalakrishnan, Hausman, Herzog, Hsu, et~al.]{brohan2022rt}
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A.~Brohan, N.~Brown, J.~Carbajal, Y.~Chebotar, J.~Dabis, C.~Finn,
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K.~Gopalakrishnan, K.~Hausman, A.~Herzog, J.~Hsu, et~al.
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\newblock Rt-1: Robotics transformer for real-world control at scale.
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\newblock \emph{arXiv preprint arXiv:2212.06817}, 2022.
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\bibitem[Brohan et~al.(2023)Brohan, Brown, Carbajal, Chebotar, Chen,
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Choromanski, Ding, Driess, Dubey, Finn, et~al.]{brohan2023rt}
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A.~Brohan, N.~Brown, J.~Carbajal, Y.~Chebotar, X.~Chen, K.~Choromanski,
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T.~Ding, D.~Driess, A.~Dubey, C.~Finn, et~al.
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\newblock Rt-2: Vision-language-action models transfer web knowledge to robotic
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control.
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\newblock In \emph{Conference on Robot Learning}, 2023.
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\bibitem[Kim et~al.(2024)Kim, Pertsch, Karamcheti, Xiao, Balakrishna, Nair,
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Rafailov, Foster, Lam, Sanketi, et~al.]{kim2024openvla}
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M.~J. Kim, K.~Pertsch, S.~Karamcheti, T.~Xiao, A.~Balakrishna, S.~Nair,
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R.~Rafailov, E.~Foster, G.~Lam, P.~Sanketi, et~al.
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\newblock Openvla: An open-source vision-language-action model.
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\newblock \emph{arXiv preprint arXiv:2406.09246}, 2024.
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\bibitem[Pertsch et~al.(2025)Pertsch, Stachowicz, Ichter, Driess, Nair, Vuong,
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Mees, Finn, and Levine]{pertsch2025fast}
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K.~Pertsch, K.~Stachowicz, B.~Ichter, D.~Driess, S.~Nair, Q.~Vuong, O.~Mees,
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C.~Finn, and S.~Levine.
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\newblock Fast: Efficient action tokenization for vision-language-action
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models.
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\newblock \emph{arXiv preprint arXiv:2501.09747}, 2025.
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\end{thebibliography}
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