Generative flow and diffusion models provide the continuous, multimodal action distributions needed for high-precision robotic policies. However, their reliance on iterative sampling introduces severe inference latency, degrading control frequency and harming performance in time-sensitive manipulation. To address this problem, we propose the One-Step Flow Policy (OFP), a from-scratch self-distillation framework for high-fidelity, single-step action generation without a pre-trained teacher. OFP unifies a self-consistency loss to enforce coherent transport across time intervals, and a self-guided regularization to sharpen predictions toward high-density expert modes. In addition, a warm-start mechanism leverages temporal action correlations to minimize the generative transport distance. Evaluations across 56 diverse simulated manipulation tasks demonstrate that a one-step OFP achieves state-of-the-art results, outperforming 100-step diffusion and flow policies while accelerating action generation by over $100\times$. We further integrate OFP into the $π_{0.5}$ model on RoboTwin 2.0, where one-step OFP surpasses the original 10-step policy. These results establish OFP as a practical, scalable solution for highly accurate and low-latency robot control.
AimBot: A Simple Auxiliary Visual Cue to Enhance Spatial Awareness of Visuomotor Policies
Computer Science and Engineering Department, University of Michigan（密歇根大学计算机科学与工程系）；Robotics Department, University of Michigan（密歇根大学机器人学系）；Lambda Labs（Lambda实验室）
A Study on Enhancing the Generalization Ability of Visuomotor Policies via Data Augmentation
University of Chinese Academy of Sciences（中国科学院大学）；School of Artificial Intelligence & Institute of Automation, Chinese Academy of Sciences & NLPR（中国科学院人工智能学院与自动化研究所）
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations
The University of Hong Kong（香港大学）；JD.com (JingDong)（京东）；Nanyang Technological University (NTU)（南洋理工大学）；Southern University of Science and Technology (SUSTech)（南方科技大学）；Huawei Technologies（华为技术有限公司）；The Chinese University of Hong Kong (CUHK)（香港中文大学）
FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency
Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）；NLPR, MAIS, Institute of Automation of Chinese Academy of Sciences（神经语言处理实验室、人工智能研究所、中国科学院自动化研究所）
eVGGT: An Efficient Geometry-Aware Vision Encoder for Visuomotor Policies
Department of Computer Vision, Mohammed bin Zayed University of Artificial Intelligence（计算机视觉系，穆罕默德·本·扎耶德人工智能大学）；AIT Austrian Institute of Technology GmbH（奥地利技术研究所）
DemoSpeedup: Accelerating Visuomotor Policies via Entropy-Guided Demonstration Acceleration
Shanghai Qi Zhi Institute（上海启智研究院）；Tsinghua University IIIS（清华大学智能科学研究院）；Shanghai AI Lab（上海人工智能实验室）；University of Electronic Science and Technology of China（电子科技大学）
EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment
Department of Automation, Tsinghua University（清华大学自动化系）；Beijing Key Laboratory of Embodied Intelligence Systems（北京具身智能系统重点实验室）；Institute for Embodied Intelligence and Robotics, Tsinghua University（清华大学具身智能与机器人研究所）
GaussFly: Contrastive Reinforcement Learning for Visuomotor Policies in 3D Gaussian Fields
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；College of Design and Engineering, National University of Singapore（新加坡国立大学设计与工程学院）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化学院）
Score and Distribution Matching Policy: Advanced Accelerated Visuomotor Policies via Matched Distillation
Westlake University（西湖大学）；Southwest University（西南大学）；Zhejiang University（浙江大学）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（北京未来区块链与隐私计算高精尖创新中心）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）
Oracle-Guided Masked Contrastive Reinforcement Learning for Visuomotor Policies
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化工程学院）
TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance
Shanghai Jiao Tong University（上海交通大学）；Xense Robotics（Xense机器人公司）；Sun Yat-sen University（中山大学）；Oxford（牛津大学）；Shanghai AI Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；UCSD（加州大学圣地亚哥分校）
Fast and Robust Visuomotor Riemannian Flow Matching Policy
Bosch Center for Artificial Intelligence（博世人工智能中心）；Division of Robotics, Perception, and Learning, KTH Royal Institute of Technology（机器人、感知与学习 division，皇家理工学院）；Computer Science Department of the Technische Universität Darmstadt（达姆施塔特技术大学计算机科学系）
