Visuomotor Policy Learning

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43 papers in the last four weeks, up 378% on the four weeks before. 0.4% of all new papers.

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Video Prediction Policy 2: Predict Better, Act Better
Oct 7, 2026Yanjiang Guo, Haodong Yan, Zhide Zhong +15Visuomotor Policy Learning World Action Models
Robotera · Tsinghua University · HKUST (GZ) +2
CAP: Codebook-Aligned Prediction for Tokenized Robot Policies
Oct 6, 2026Haoran Chen, Jingtian Ji, Samuel Wheeler +2Robot Policy Learning Robot Learning
Toyota Technological Institute at Chicago · Argonne National Laboratory
MIM-VLA: Learning Physical Interaction Representations from Gripper Motor Feedback
Oct 6, 2026Jaeyoung Lee, Jiyeon Koo, Taehwa Kim +2Robotic Grasping Visuomotor Policy Learning
School of Computing, iRASC Lab., Gachon University, Seongnam, Republic of Korea
VOMMI: Collecting and Leveraging Portable Demonstrations for Mobile Manipulation
Oct 6, 2026Yutian Zhang, Xingrui Xiong, Siyuan Ma +12Mobile Manipulation Robotic Data Collection
ZJU · Yale · THU +5
ViDAL: A Visual Dynamics-Grounded Action Latent Space for Vision-Language-Action Models
Oct 6, 2026Yuan Xu, Yixiang Chen, Qisen Ma +10Latent Action Learning Visuomotor Policy Learning
CASIA · UCAS · Alibaba Group +1
IronMan: Information-Constrained Video-Action Learning for Robot Manipulation
Oct 6, 2026Yuanshuo Zhang, Wenzhe Zhao, Zixing Lei +2Information Bottleneck Generalization in Robotic Manipulation
Shanghai Jiao Tong University · Joy Future Academy, JD
Bilinear Flow Policy: Distributional Extrapolation for Goal-Conditioned Visuomotor Imitation
Oct 5, 2026Wonsuhk Jung, Sundhar Vinodh Sangeetha, Chen Xu +4OOD Generalization Visuomotor Policy Learning
Georgia Institute of Technology · Toyota Research Institute · University of Washington
Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations
Oct 5, 2026Ryusei Matsumoto, Mei Kinjo, Yoko Sasaki +1Visuomotor Policy Learning Multi-Robot Coordination
National Institute of Advanced Industrial Science and Technology (AIST), Japan · Institute of Science Tokyo, Japan · The University of Tokyo, Japan
VAMPS: Visual and Motor Policies from Sampling-Based Planning
Oct 4, 2026Mohamed Yassine Kabouri, Pietro Noah Crestaz, Quang-Nam Nguyen +3Robot Policy Learning Visuomotor Policy Learning
LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France. · Machines in Motion Laboratory, New York University, New York, USA. · Industrial Engineering Department, University of Trento, Trento, Italy. +1
Recursive Self-Improvement of Visuomotor Policies through Local Recovery Supervision
Oct 4, 2026Yuzhi Zhang, Xinyu Liu, Yu ZhangRobot Failure Recovery Visuomotor Policy Learning
Beihang University
Whole-Body Aerial Grasping and Lifting via Partial Visual Observations
Sep 30, 2026Jiaye Jin, Rui Jin, Xinhang Xu +6Aerial Robotics Partially Observable RL
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 · College of Electronics and Information Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China · NTU–VinUni Joint Research Laboratory for Embodied AI and Robotics, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, and VinUniversity, Hanoi, Vietnam
Text-to-3D Policy: Fine-Grained Language-Behavior Alignment for Unseen Specification Generalization
Sep 30, 2026Xinhao Yang, Wenhao Wu, Ning Lv +5Language-Conditioned Robot Manipulation Generalization in Robotic Manipulation
Nanjing University · Australian National University · University of Technology Sydney
MotionWeave: Learning Motion-Centered Future Dynamics for Vision-Language-Action Policies
Sep 30, 2026Jingqiu Wang, Yan WangVisuomotor Policy Learning Robotic Manipulation
School of Data Science and Engineering East China Normal University, Shanghai, China
A Biophysically Detailed C. elegans Circuit as a Task-Agnostic Dynamical Core for Visually Robust Robot Manipulation
Sep 30, 2026Linrui Qian, Jiajia Zhang, Gan He +7Robotic Control Robot Policy Learning
CogLeap.AI Space Intelligence (Wuxi) Technology Co., Ltd., Beijing 100080, China · School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China · Institute for Brain and Intelligence, Fudan University, Shanghai 200433, China. +1
BIND: Binding 3D Robot Actions to 2D Image Features
Sep 29, 2026Cameron Smith, Arsh Tangri, Vitor Guizilini +3Visuomotor Policy Learning Data-Efficient Robot Learning
University of Southern California · Toyota Research Institute
Rethinking Representations for World-Action Modeling
Sep 29, 2026Haoyi Jiang, Liu Liu, Xinjiang Wang +12World Model Learning Action-Conditioned World Models
Huazhong University of Science & Technology · Horizon Robotics · Fudan University +2
Remember What You Did: Action-History Memory with Dual-Expert Denoising for Long-Horizon Vision-Language-Action Policies
Sep 29, 2026Yaxin Zhao, Dianye Huang, Chenwei Wang +2Memory-Augmented VLMs Long-Horizon Robotic Manipulation
Harbin Institute of Technology, Harbin, China. · Medical Intelligence and Robotic Cognition (MIRoC) Lab, Department of Mechanical Engineering, The University of Hong Kong (HKU), Hong Kong SAR, China. · Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China
V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents
Sep 29, 2026Yang Zhang, Jiangyuan Zhao, Chenyou Fan +4Visual Representation Learning Visuomotor Policy Learning
Tsinghua University · Shanghai Jiao Tong University · Fudan University +4
ComManip: Overfitting Manipulation Policies to Comfortable Regions
Sep 29, 2026Yidan Lai, Xinyi Chen, Shuquan Man +1Visuomotor Policy Learning Robotic Manipulation
South China University of Technology, Guangzhou, China · Nanyang Technological University, Singapore
LexiconVLA: Learning Reusable Atomic Action Codebooks for Unseen Tasks
Sep 29, 2026Zeming Wei, Jianheng Ye, Xinshuai Song +3Visuomotor Policy Learning Robotic Manipulation
Sun Yat-sen University · X-Era AI Lab · Pengcheng Laboratory
Where Predictive Supervision Goes Shapes What VLA Policies Learn
Sep 29, 2026Hanseul Kim, Jewon Yeom, Youngjoon Jeong +2Visuomotor Policy Learning Vision-Language-Action Models
Graduate School of Data Science Seoul National University
Gaze Prompts: Temporally Dense Human Attention for Vision-Language-Action Fine-Tuning
Sep 28, 2026Yihan Zhou, Rui Yan, Mingcong Li +4Visuomotor Policy Learning Robotic Manipulation
Department of Automation, Tsinghua University · Beijing Key Laboratory of Embodied Intelligence Systems · Institute for Embodied Intelligence and Robotics, Tsinghua University +1
Quantile Head for Vision-Language-Action Models
Sep 28, 2026Xuan Wang, Yinan Wu, Haoran Duan +1Quantile Regression Efficient VLA Model Inference
Department of Automation Tsinghua University
Estimate, Don't Imitate: Reusing Differentiable State-Based Policies for Visuomotor Control
Sep 27, 2026Denis Shcherba, Adrian Abel, Eckart Cobo-Briesewitz +3Visuomotor Policy Learning Robotic Manipulation
Fraunhofer Heinrich-Hertz-Institut, Berlin, Germany · Technische Universität Berlin, Germany
SLIP-VLA: Single-Step Latent Imagination for Policy Learning in Vision-Language-Action Models
Sep 27, 2026Tianfu Li, Haoxuan Xu, Wenbo Chen +7World Model Learning Visuomotor Policy Learning
The Hong Kong University of Science and Technology (Guangzhou). · The Hong Kong University of Science and Technology. · Nanyang Technological University. +1
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
ActGaze: Learning Action-Grounded Gaze through Counterfactual Visual Interventions for High-Precision Manipulation
Sep 24, 2026Jinxuan Zhu, Jiaheng Wang, Chao Tang +7Visuomotor Policy Learning Robotic Manipulation
Samsung Robotics eXperience · National University of Singapore
Morphometric Imitation: From Morphology and Contact Aware Hand Retargeting to Sim-to-Real Visuomotor Policy
Sep 23, 2026Tara Sadjadpour, Siming He, C. K. Wolfe +5Zero-Shot Robotic Manipulation Contact-Rich Robotic Manipulation
Department of Electrical Engineering and Computer Science at the University of California, Berkeley, CA, USA
Median Temporal Ensembling: Training-Free Robust Aggregation for Action-Chunked Visuomotor Policies
Sep 22, 2026Yuhang JiangEnsemble Learning Adversarial Robustness
University of Trento, Italy
MAVP: Map-Aware Visuomotor Policies for Mobile Manipulation
Sep 22, 2026Jinhe Tang, Ruixiao Dai, Weiming ZhiRobot Trajectory Tracking Robot Policy Learning
School of Computer Science, The University of Sydney, Australia

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

Video Prediction Policy 2: Predict Better, Act Better

Yanjiang Guo, Haodong Yan, Zhide Zhong, Zhongru Zhang+14

Robotera · Tsinghua University · HKUST (GZ)+2

Visuomotor Policy Learning World Action Models Zero-Shot Robotic Manipulation Video Prediction

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World action models (WAMs) have emerged as an important class of generalist robot policies, aiming to transfer video prediction priors to action learning. However, we find that existing WAMs frequently produce incorrect motion predictions in open-ended environment, leading to erroneous actions. We attribute this limitation to two factors: (1) base video models are not optimized for manipulation, and (2) naively incorporating action components into video models can substantially degrade their generalization capabilities. We introduce Video Prediction Policy 2 (VPP2), a WAM that enables strong zero-shot generalization in both video prediction and action generation. First, we curate a large-scale, diverse dataset of manipulation videos to continue pretraining the base video foundation model. We annotate video clips with detailed captions and perform \textit{event-level} video pretraining to promote generalization across open-ended manipulation tasks. Second, we post-train and distill the video model into a single-step visual planner with fixed prediction horizon. Finally, we introduce action module via a mixture-of-transformers (MoT) architecture to learn implicit inverse dynamics model. Experiments demonstrate three key results: (1) VPP2-14B outperforms Cosmos3-64B by 11.0% points in video prediction instruction-following success rate on open-ended tasks; (2) VPP2 surpasses the strongest baseline by 18.5% points in success rate on real-world zero-shot ALOHA manipulation tasks; and (3) following benchmark-specific post-training, VPP2 achieves the highest success rates among evaluated methods on the challenging LIBERO-Pro, LIBERO-OOD, and RoboDojo benchmarks.

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