Streaming Flow Policy: Simplifying diffusion/flow-matching policies by treating action trajectories as flow trajectories

Computational approaches to motor learning by imitation

S. Schaal, A. Ijspeert, and A. Billard · 2003

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Imitation learning of globally stable non-linear point-to-point robot motions using nonlinear programming

S. M. Khansari-Zadeh and A. Billard · 2010

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Learning stable nonlinear dynamical systems with gaussian mixture models

S. M. Khansari-Zadeh and A. Billard · 2011

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Dynamical movement primitives: learning attractor models for motor behaviors

A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal · 2013

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Deep unsupervised learning using nonequilibrium thermodynamics

J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015

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Neural ordinary differential equations

R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018

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FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models

W. Grathwohl, R. T. Q. Chen, J. Bettencourt, and D. Duvenaud · 2019

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Denoising diffusion probabilistic models

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J. Ho, A. Jain, and P. Abbeel · 2020

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Neural controlled differential equations for irregular time series

P. Kidger, J. Morrill, J. Foster, and T. Lyons · 2020

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What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín · 2021

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Denoising diffusion implicit models

J. Song, C. Meng, and S. Ermon · 2021

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Implicit behavioral cloning

P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson · 2022

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Planning with Diffusion for Flexible Behavior Synthesis

M. Janner, Y. Du, J. Tenenbaum, and S. Levine · 2022

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Multiscale sensor fusion and continuous control with neural CDEs

S. Singh, F. M. Ramirez, J. Varley, A. Zeng, and V. Sindhwani · 2022

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Diffusion policy: Visuomotor policy learning via action diffusion

C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song · 2023

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Flow matching for generative modeling

Y. Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le · 2023

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Provable guarantees for generative behavior cloning: Bridging low-level stability and high-level behavior

Original

A. Block, A. Jadbabaie, D. Pfrommer, M. Simchowitz, and R. Tedrake · 2023

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Learning fine-grained bimanual manipulation with low-cost hardware

NoMaD: Goal masked diffusion policies for navigation and exploration

A. Sridhar, D. Shah, C. Glossop, and S. Levine · 2024

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Learning deep dynamical systems using stable neural ODEs

A. Sochopoulos, M. Gienger, and S. Vijayakumar · 2024

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Scaling rectified flow transformers for high-resolution image synthesis

P. Esser, S. Kulal, A. Blattmann, R. Entezari, J. Müller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel, et al · 2024

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SiT: Exploring flow and diffusion-based generative models with scalable interpolant transformers

N. Ma, M. Goldstein, M. S. Albergo, N. M. Boffi, E. Vanden-Eijnden, and S. Xie · 2024

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Pyramidal flow matching for efficient video generative modeling

Original

Y. Jin, Z. Sun, N. Li, K. Xu, H. Jiang, N. Zhuang, Q. Huang, Y. Song, Y. Mu, and Z. Lin · 2024

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