Flow Matching

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79 papers in the last four weeks, up 365% on the four weeks before. 0.8% of all new papers.

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Morphologically Equivariant Flow Matching for Bimanual Mobile Manipulation
May 12, 2026Max Siebenborn, Daniel Ordoñez Apraez, Sophie Lueth +4Flow Matching Mobile Manipulation
PEARL Lab, Dept. of Computer Science, TU Darmstadt, Germany · 2Istituto Italiano di Tecnologia, 3Hessian.AI, 4Robotics Institute Germany
Expected Batch Optimal Transport Plans and Consequences for Flow Matching
May 12, 2026Samuel Boïté, Julie Delon, Kimia NadjahiFlow Matching Optimal Transport
1DMA, ENS Paris · Université Paris Cité · DI, ENS Paris, CNRS
When Policy Entropy Constraint Fails: Preserving Diversity in Flow-based RLHF via Perceptual Entropy
May 12, 2026Xiaofeng Tan, Jun Liu, Bin-Bin Gao +5Flow Matching Generative Modeling
Southeast University · Tencent Youtu Lab · Southern University of Science and Technology
Sharpen Your Flow: Sharpness-Aware Sampling for Flow Matching
May 12, 2026Aditi Gupta, Soon Hoe Lim, Annan Yu +1Flow Matching Flow-Based Generative Modeling
Lawrence Berkeley National Laboratory · International Computer Science Institute · 1Lawrence Berkeley National Laboratory, 2International Computer Science Institute +4
Operator-Guided Model Reduction for Generative Sampling in Lattice Field Theory
May 11, 2026Moxian QianFlow Matching Markov Chain Monte Carlo
Johannes Gutenberg University Mainz, 55128 Mainz, Germany
Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow-Matching Models
May 11, 2026Andreas Bergmeister, Stefanie Jegelka, Nikolas Nüsken +2Diffusion Model Alignment Flow Matching
1TU Munich, MCML · 2MIT CSAIL · 3King’s College London +2
Follow the Mean: Reference-Guided Flow Matching
May 11, 2026Pedro M. P. Curvo, Maksim Zhdanov, Floor Eijkelboom +1Flow Matching Flow-Based Generative Modeling
University of Amsterdam · AMLab
Kinetic-Optimal Scheduling with Moment Correction for Metric-Induced Discrete Flow Matching in Zero-Shot Text-to-Speech
May 10, 2026Dong Yang, Yiyi Cai, Haoyu Zhang +2Flow Matching TTS Synthesis
1The University of Tokyo · 2Independent Researcher
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
Physics-Informed Neural PDE Solvers via Spatio-Temporal MeanFlow
May 9, 2026Hanru Bai, Yuncheng Zhou, Difan ZouFlow Matching Neural PDE Solvers
1Fudan University · 2The University of Hong Kong.
Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions
May 9, 2026Le-Tuyet-Nhi Pham, Giovanni Conforti, Zhenjie Ren +1Flow Matching Generative Modeling
École Polytechnique, CMAP, IP Paris Route de Saclay, 91120 Palaiseau, France · Università degli Studi di Padova, Via Trieste, 63, 35131 Padova, Italia · LaMME, Université Évry Paris-Saclay Boulevard François Mitterrand 91000, France
Restoration-Aligned Generative Flow Models for Blind Motion Deblurring
May 9, 2026Insoo Kim, Jinwoo ShinFlow Matching Image Deblurring
1NAVER Cloud · 2KAIST AI
ElasticFlow: One-Step Physics-Consistent Policy with Elastic Time Horizons for Language-Guided Manipulation
May 9, 2026Kewei Chen, Yayu Long, Shuai Li +1Flow Matching Long-Horizon Robotic Manipulation
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences · Chongqing School, University of Chinese Academy of Sciences · Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
Deterministic Decomposition of Stochastic Generative Dynamics
May 9, 2026Xingyu Song, Yuan Mei, Naoya TakeishiFlow Matching Stochastic Differential Equations
1The University of Tokyo · 2Zhejiang University · *Visiting student at The University of Tokyo from Zhejiang University.
FLUX: Geometry-Aware Longitudinal Flow Matching with Mixture of Experts
May 9, 2026Josue Ortega Caro, Yongxu Zhang, Hannah M Batchelor +3Dynamical Systems Flow Matching
Yale University
MC-RFM: Geometry-Aware Few-Shot Adaptation via Mixed-Curvature Riemannian Flow Matching
May 8, 2026Salim Khazem, Ibrahim Mohamed Serouis, Zakaria EzzahedFlow Matching Vision Foundation Model Adaptation
Talan Research Center, Paris, France
Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning
May 8, 2026Riad Ahmed, Sujosh Nag, Moniruzzaman Akash +2Flow Matching Robot Policy Learning
University of New Hampshire, Durham, NH, USA
Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
May 8, 2026Moritz Piening, Richard Duong, Gabriele SteidlFlow Matching Generative Modeling
Institut für Mathematik, Technische Universität Berlin, Germany
Exploring and Exploiting Stability in Latent Flow Matching
May 8, 2026Rania Briq, Michael Kamp, Ohad Fried +2Flow Matching Efficient Inference
Forschungszentrum Jülich · Technical University Dortmund · Lamarr Institute +4
What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching
May 8, 2026Alec Helbling, Sebastian Gutierrez Hernandez, Benjamin Hoover +2Flow Matching Flow-Based Generative Modeling
Georgia Tech · IBM Research
Flow-OPD: On-Policy Distillation for Flow Matching Models
May 8, 2026Zhen Fang, Wenxuan Huang, Yu Zeng +8Diffusion Model Alignment Flow Matching
University of Science and Technology of China · University of California, Los Angeles · 3The Chinese University of Hong Kong +1
P-Flow: Proxy-gradient Flows for Linear Inverse Problems
May 8, 2026Zehua Jiang, Fenghao Zhu, Xinquan Wang +2Flow Matching Inverse Problems
University of Notre Dame Indiana, USA
Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation
May 8, 2026Amin Karimi Monsefi, Dominic Culver, Nikhil Bhendawade +3Flow Matching Diffusion Language Model Inference
1The Ohio State University · 2Apple
Flow Matching for Count Data
May 8, 2026Ganchao Wei, John PearsonFlow Matching Synthetic Data Generation
Department of Neurobiology Department of Statistical Science Duke University Durham, NC, USA · Department of Neurobiology Department of Electrical and Computer Engineering Duke University Durham, NC, USA
Structured Coupling for Flow Matching
May 8, 2026Xavier Sumba, Carles Balsells-Rodas, Yingzhen LiFlow Matching Disentangled Representation Learning
Imperial College London
Debiased Counterfactual Generation via Flow Matching from Observations
May 8, 2026Hugh Dance, Johnny Xi, Peter Orbanz +1Flow Matching Causal Counterfactual Generation
Gatsby Computational Neuroscience Unit University College London · Department of Statistics University of British Columbia
Tessellations of Semi-Discrete Flow Matching
May 8, 2026Emile Pierret, Johannes Hertrich, Samuel Hurault +1Flow Matching
DMA, ENS-PSL, MAP5 · DMA, ENS-PSL · LIGM, Univ. Gustave Eiffel, CNRS
Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting
May 7, 2026Fan Xu, Yuan Gao, Kun Wang +4Flow Matching Probabilistic Forecasting
1USTC · 2SLAI · 3THU +3
Path-Coupled Bellman Flows for Distributional Reinforcement Learning
May 7, 2026Boyang Xu, Qing Zou, Siqin Yang +1Distributional RL Flow Matching
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.

May 12, 2026, cs.ROJ/K move · Enter open · S save

Morphologically Equivariant Flow Matching for Bimanual Mobile Manipulation

Max Siebenborn, Daniel Ordoñez Apraez, Sophie Lueth, Giulio Turrisi+3

PEARL Lab, Dept. of Computer Science, TU Darmstadt, Germany · 2Istituto Italiano di Tecnologia, 3Hessian.AI, 4Robotics Institute Germany

Flow Matching Mobile Manipulation Bimanual Robotic Manipulation Generalization in Robotic Manipulation Robotics Robot Imitation Learning

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Mobile manipulation requires coordinated control of high-dimensional, bimanual robots. Imitation learning methods have been broadly used to solve these robotic tasks, yet typically ignore the bilateral morphological symmetry inherent in such systems. We argue that morphological symmetry is an underexplored but crucial inductive bias for learning in bimanual mobile manipulation: knowing how to solve a task in one configuration directly determines how to solve its mirrored counterpart. In this paper, we formalize this symmetry prior and show that it constrains optimal bimanual policies to be ambidextrous and equivariant under reflections across the robot's sagittal plane. We introduce a

\mathbb{C}_2

-equivariant flow matching policy that enforces reflective symmetry either via a regularized training loss or an equivariant velocity network. Across planar and 6-DoF mobile manipulation tasks, symmetry-informed policies consistently improve sample efficiency and achieve zero-shot generalization to mirrored configurations absent from the training distribution. We further validate this zero-shot generalization capability on a real-world manipulation task with a TIAGo++ robot. Together, our findings establish morphological symmetry as an effective, generalizable, and scalable inductive bias for ambidextrous generative policy learning.

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