Mean Flow

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Adaptive Mean Flow for Responsive Closed-Loop Robot Control
Oct 5, 2026Aksel Vaaler, Marco Job, Christian Holden +1Robotic Control Flow Matching
Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU)
Faster Visuomotor Policy Learning on Action Manifolds via Riemannian MeanFlow
Sep 24, 2026S. Talha Bukhari, Austin Garrett, Yi Wei +3Visuomotor Policy Learning Riemannian Flow Matching
Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
The Structure of Merging Turbulent Jets Beneath a Small Quadrotor
Aug 31, 2026Anoop Kiran, Nora Ayanian, Kenneth BreuerMean Flow
Center for Fluid Mechanics, School of Engineering, Brown University, Providence, Rhode Island, USA · Department of Computer Science, Brown University, Providence, Rhode Island, USA
GraspMeanFlow: SE(3)-Equivariant MeanFlow for Few-Step 6-DoF Grasp Generation
Aug 4, 2026Jiyong Kwon, Yikun Bai, Amirhossein Mollaali +1Flow Matching Robotic Grasping
School of Mechanical Engineering, Purdue University · Department of Mathematics, Purdue University
SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups
Jul 29, 2026Yikun Bai, Binghang Lu, Yikai Liu +7Flow Matching Lie Group Methods
Department of Mathematics, Purdue University · School of Electrical and Computer Engineering, Purdue University · Department of Biochemistry, Purdue University +5
FdAudio: MeanFlow-Anchored Fréchet-Distance Post-Training for One-Step Text-to-Audio Generation
Jul 11, 2026Kuan-Po Huang, Bo-Ru Lu, Ho-Lam Chung +2Text-to-Audio Generation Flow Matching
National Taiwan University · Amazon
FlashTTS: Fast Streaming TTS with MTP Acceleration and X-pred Mean Flow Distillation
Jun 8, 2026Hanke Xie, Xiaming Ren, Dake Guo +10Flow Matching Multi-Token Prediction
Huawei Technologies Co., Ltd
Score-Based One-step MeanFlow Policy Optimization
May 22, 2026Kyungyoon Kim, Donghyeon Ki, Hee-Jun Ahn +1Flow Matching MeanFlow
Korea University, Decision Making Lab · Gauss Labs Inc.
Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent
May 20, 2026Zeyuan Wang, Da Li, Yulin Chen +6Reinforcement Learning Maximum Entropy RL
Laboratory for Big Data and Decision, National University of Defense Technology, China · Samsung AI Center Cambridge, UK · Queen Mary University of London, UK +3
Stabilizing, Scaling & Enhancing MeanFlow for Large-scale Diffusion Distillation
May 18, 2026Xiao He, Yang Li, Peizhen Zhang +3Flow Matching Diffusion Model Distillation
1State Key Laboratory of Integrated Services Networks, Xidian University · 2Tencent Hunyuan
Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
May 12, 2026Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed +2Markov Models MeanFlow
Department of Computer Science, University of Kentucky Lexington, Kentucky 40506, USA
Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement
May 10, 2026Wei Zhu, Kai Zhang, Yu Zheng +3Flow Matching Diffusion Model Distillation
Nanjing University of Science and Technology · Nanjing University · Huawei
On Variance Reduction in Learning Mean Flows
May 10, 2026Juanwu Lu, Ziran WangFlow Matching MeanFlow
Purdue University
Point-MF: One-step Point Cloud Generation from a Single Image via Mean Flows
Apr 27, 2026Yuta Baba, Keiji YanaiFlow Matching One-Step Generative Modeling
The University of Electro-Communications 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan
Mean Flow Policy Optimization
Apr 16, 2026Xiaoyi Dong, Xi Sheryl Zhang, Jian ChengMeanFlow Mean Flow
C2DL, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · AiRiA +1
Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
Jan 29, 2026Winfried Ripken, Michael Plainer, Gregor Lied +5Dynamical Systems Flow Map Learning
Technical University Berlin · BIFOLD · Free University of Berlin +6

Oct 5, 2026, cs.ROJ/K move · Enter open · S save

Adaptive Mean Flow for Responsive Closed-Loop Robot Control

Aksel Vaaler, Marco Job, Christian Holden, Olav Egeland

Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU)

Robotic Control Flow Matching Mean Flow Robot Imitation Learning

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Diffusion- and flow-based robot policies have recently become widespread in robotic Imitation Learning (IL) due to their high performance and ability to model continuous and multimodal distributions. However, the iterative denoising procedure used by these models introduces significant prediction latency, hindering high-frequency closed-loop robot control and leading to jittery, unstable motion when frequent updates to the robot's action predictions are used. Therefore, it is common practice to train models to predict chunks of actions that can be executed sequentially without feedback, even when this reduces responsiveness and may mean the most recent state information is not used. In this article, we present Adaptive Mean Flow (AMF), a flow-based IL method that enables smooth and responsive, fully closed-loop robot control. AMF uses Mean Flow, which is an accelerated form of Flow Matching (FM), to minimize prediction latency. To ensure smoothness and consistency across predictions, AMF uses a corrupted version of the trajectory from the previous step when predicting new robot actions, with the signal-to-noise ratio increasing over the time parameter of the trajectory. This discourages large changes in the prediction from one step to the next, while allowing freedom to adapt the predictions for future steps. We evaluate AMF across a wide range of simulated and real robot tasks and demonstrate significantly improved performance compared with baselines. Code: https://github.com/akselva/Adaptive-mean-flow-RoboticIL.

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