Visuomotor Policy Learning

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Overview

Visuomotor policies map what a robot sees to actions, helping it manipulate objects, navigate, and interact when the scene or task changes. Common approaches learn from demonstrations through action-chunking transformers, diffusion or flow-matching policies, and vision-language-action models; reinforcement learning and policy distillation are also used. Researchers improve these policies with geometric and object-centered visual features, memory, contact sensing, and predictions of how actions will change the scene, evaluating them on benchmarks such as LIBERO and RoboTwin as well as physical robots. A central tension is achieving reliable generalization and safe, precise control in real settings while keeping data needs, model size, and inference latency low.

Latest papers

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286 papers54 papers in the last four weeks, up 29% on the four weeks before. 0.3% of all new papers.

  1. Risk-Aware Reinforcement Learning for Mobile Manipulation

    Mar 4, 2026Michael Groom, James Wilson, Nick Hawes +1Risk-Sensitive RLMobile Manipulation

    Oxford Robotics Institute, Department of Engineering Science, University of Oxford, Oxford, United Kingdom · Dyson Institute of Engineering & Technology, Malmesbury, United Kingdom · Bristol Robotics Laboratory, School of Engineering, University of West England, Bristol, United Kingdom

  2. CHOP: Counterfactual Human Preference Labels Improve Obstacle Avoidance in Visuomotor Navigation Policies

    Mar 2, 2026Gershom Seneviratne, Jianyu An, Vaibhav Shende +6Safe Robot NavigationVisuomotor Policy Learning

    University of Maryland, College Park

  3. Neural Implicit Action Fields: From Discrete Waypoints to Continuous Functions for Vision-Language-Action Models

    Mar 2, 2026Haoyun Liu, Jianzhuang Zhao, Xinyuan Chang +11Vision-Language-Action ModelsVisuomotor Policy Learning

    State Key Laboratory for Novel Software Technology, Nanjing University · Faculty of Computility Microelectronics, Shenzhen University of Advanced Technology · Amap, Alibaba Group +5

  4. Neural Fields as World Models

    Feb 21, 2026Joshua NunleyWorld Model LearningAction-Conditioned World Models

    Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington · Cognitive Science Program, Indiana University, Bloomington

  5. Mind the Gap: Rethinking I/O Design for Contact-Rich Visuomotor Policy Learning

    Feb 9, 2026Cuijie Xu, Shurui Zheng, Zihao Su +7Contact-Rich Robotic ManipulationRobot Teleoperation

    Department of Electronic Engineering, Tsinghua University · Qiuzhen College, Tsinghua University · School of Mechanical and Aerospace Engineering, Jilin University +2

  6. Think Proprioceptively: State-Grounded Visual Token Selection for VLA Policies

    Feb 6, 2026Fangyuan Wang, Peng Zhou, Jiaming Qi +4Efficient VLM InferenceVisuomotor Policy Learning

    The Hong Kong Polytechnic University · Great Bay University · Northeast Forestry University +2

  7. PACT-WAM: Predicting Actions and Visual Foresight with Compact Temporal Encoding for Robot Manipulation

    Feb 5, 2026Yushan Liu, Jingjing Fan, Shoujie Li +3World Models for RoboticsVisuomotor Policy Learning

    Tsinghua University · Nanyang Technological University · Xspark AI

  8. ISS Policy : Scalable Diffusion Policy with Implicit Scene Supervision

    Dec 17, 2025Wenlong Xia, Jinhao Zhang, Ce Zhang +6Robot Skill LearningVisuomotor Policy Learning

    [cs.RO]

  9. HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies

    Dec 5, 2025Zhiying Du, Bei Liu, Yaobo Liang +7Visuomotor Policy LearningVision-Language-Action Models

    1Fudan University · 2Microsoft Research Asia · 3Xi’an Jiaotong University +1

  10. SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment

    Nov 11, 2025Rong Xue, Jiageng Mao, Mingtong Zhang +1Rectified FlowVisuomotor Policy Learning

    University of Southern California

  11. Learning Robust Visuomotor Policies via Dynamics Alignment

    Oct 31, 2025Dohyeok Lee, Jung Min Lee, Munkyung Kim +7Visuomotor Policy LearningFlow-Based Policy Learning

    Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea · Department of Statistics, Korea University, Seoul 02841, Korea

  12. Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation

    Oct 30, 2025Georgios Kamaras, Craig Innes, Subramanian RamamoorthySimulation-Based InferenceAdaptive Inference

    School of Informatics, The University of Edinburgh, EH8 9AB, UK. · Heriot-Watt University · UKRI Turing AI World Leading Researcher Fellowship on AI for Person-Centred and Teachable Autonomy

  13. eVGGT: An Efficient Geometry-Aware Vision Encoder for Visuomotor Policies

    Sep 19, 2025An Dinh Vuong, Minh Nhat Vu, Ian ReidVisual Representation LearningVisuomotor Policy Learning

    Department of Computer Vision, Mohammed bin Zayed University of Artificial Intelligence, UAE · AIT Austrian Institute of Technology GmbH, Austria

  14. Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution

    Aug 8, 2025Zhanyi Sun, Shuran SongVisuomotor Policy LearningDiffusion Policy

    Stanford University

  15. Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers

    Jul 21, 2025Ian Chuang, Jinyu Zou, Andrew Lee +2Visuomotor Policy LearningEfficient ViTs

    University of California, Berkeley · 2Tongji University · University of California, Davis

  16. Wavelet Policy: Imitation Learning in the Scale Domain with World Prior Memory

    Apr 7, 2025Changchuan Yang, Haoxuan Xu, Yuhang Dong +3Latent Action LearningVisuomotor Policy Learning

    Zhejiang University · The Hong Kong University of Science and Technology · Tsinghua University, DISCOVER Robotics