Vision-Language-Action Models

Also known as VLA

These models combine vision-language backbones with action generators that produce continuous motion or discrete action tokens, often in chunks and with proprioceptive input. They are commonly trained on robot demonstrations, with flow-matching and diffusion policies, reinforcement fine-tuning, and predictive world models used to improve control and planning. Research spans tabletop and bimanual manipulation, mobile and aerial navigation, autonomous driving, and humanoid control, evaluated on benchmarks such as LIBERO, CALVIN, RoboTwin, and driving simulators. A central challenge is making broad visual and language competence translate into reliable closed-loop behavior under new objects, viewpoints, embodiments, delays, and safety constraints; benchmark success can still conceal weak instruction grounding and sim-to-real failures.

Latest papers

1,202 papers131 papers in the last four weeks, down 41% on the four weeks before. 0.9% of all new papers.

DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI
Feb 16, 2026En Yu, Haoran Lv, Jianjian Sun +46Flow Matching for Action Generation Vision-Language-Action Models
WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
Feb 15, 2026Zhennan Jiang, Shangqing Zhou, Yutong Jiang +11World Models for Robotics World Models
University of Chinese Academy of Sciences · Institute of Automation, Chinese Academy of Sciences · Zhongguancun Academy +2
LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation
Feb 12, 2026Yue Hu, Avery Xi, Qixin Xiao +4Robot Navigation Vision-Language Navigation
University of Michigan, Ann Arbor
H-WM: Robotic Task and Motion Planning Guided by Hierarchical World Model
Feb 11, 2026Jinbang Huang, Wenyuan Chen, Zhiyuan Li +9Long-Horizon Robotic Manipulation World Models for Robotics
Huawei Noah’s Ark Lab · University of Toronto · University of British Columbia +1
Think Proprioceptively: State-Grounded Visual Token Selection for VLA Policies
Feb 6, 2026Fangyuan Wang, Peng Zhou, Jiaming Qi +4Efficient VLM Inference Visuomotor Policy Learning
The Hong Kong Polytechnic University · Great Bay University · Northeast Forestry University +2
MobileManiBench: Simplifying Model Verification for Mobile Manipulation
Feb 5, 2026Wenbo Wang, Fangyun Wei, QiXiu Li +5Mobile Manipulation Robotics Simulation
University of Sydney · 1Microsoft Research Asia · 3Tsinghua University
DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation
Jan 29, 2026Haozhe Xie, Beichen Wen, Jiarui Zheng +4Inference-Time Optimization Efficient VLM Inference
TIDAL: Temporally Interleaved Diffusion and Action Loop for High-Frequency VLA Control
Jan 21, 2026Yuteng Sun, Haoran Wang, Ruofei Bai +4Robotic Control Diffusion Policy
Institute for Infocomm Research (I2R), A*STAR, Singapore · 2Tsinghua University, Beijing, China · 3Nanyang Technological University, Singapore
NeuroVLA: A Brain-inspired Embodied Intelligence for Fluid and Fast Reflexive Robotics Control
Jan 21, 2026Weiyu Guo, He Zhang, Pengteng Li +7Robotic Control Vision-Language-Action Models
The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. · AI2 Robotics, Shenzhen, China · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China +1
V-VLAPS: Value-Guided Planning for Vision-Language-Action Models
Jan 2, 2026Ke Ren, Ali Salamatian, Kieran Pattison +1Monte Carlo Tree Search Robotic Manipulation
The University of British Columbia.
VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models
Dec 27, 2025Borong Zhang, Jiahao Li, Jiachen Shen +7Long-Horizon Planning Robotics
Institute for Artificial Intelligence, Peking University. · Zhongguancun Academy. · PKU-PsiBot Joint Lab. +2
StereoVLA: Enhancing Vision-Language-Action Models with Stereo Vision
Dec 26, 2025Shengliang Deng, Mi Yan, Yixin Zheng +7Visual Spatial Reasoning Robotic Manipulation
Galbot · The University of Hong Kong · CFCS, School of CS, Peking University +3
ActionFlow: A Pipelined Action Acceleration for Vision Language Models on Edge
Dec 23, 2025Yuntao Dai, Hang Gu, Teng Wang +6Efficient VLA Model Inference Vision-Language-Action Models
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China · Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China · IEIT SYSTEMS Co., Ltd., Beijing, China
VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer
Dec 9, 2025Songqiao Hu, Zeyi Liu, Shuang Liu +5Robot Safety Vision-Language-Action Models
Department of Automation, Tsinghua University, Beijing 100084, China. · Institute for Embodied Intelligence and Robotics, Tsinghua University, Beijing 100084, China. · 3TetraBOT. +1
HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies
Dec 5, 2025Zhiying Du, Bei Liu, Yaobo Liang +7Visuomotor Policy Learning Vision-Language-Action Models
1Fudan University · 2Microsoft Research Asia · 3Xi’an Jiaotong University +1
Transport Discrepancy as a Reliability Signal for Vision-Language-Action Models
Dec 1, 2025Wanpeng Zhang, Ye Wang, Hao Luo +6Uncertainty Estimation for VLA Models Vision-Language-Action Models
1Peking University · 3BeingBeyond · 2Renmin University of China
Continually Evolving Skill Knowledge in Vision Language Action Model
Nov 22, 2025Yuxuan Wu, Guangming Wang, Zhiheng Yang +4Continual Learning for VLA Models Continual Robot Learning
Shanghai Jiao Tong University · Shanghai Innovation Institute · University of Cambridge +4
XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations
Nov 4, 2025Shichao Fan, Kun Wu, Zhengping Che +12Representation Learning Robot Skill Learning
Beijing Innovation Center of Humanoid Robotics, Beijing, China · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China · State Key Laboratory of Virtual Reality Technology and Systems, SCSE, Beihang University, Beijing, China +1
A Survey on Efficient Vision-Language-Action Models
Oct 27, 2025Zhaoshu Yu, Bo Wang, Pengpeng Zeng +7Model Compression Robotic Data Collection
School of Computer Science and Technology, Tongji University, China · School of Computing and Artificial Intelligence, Southwest Jiaotong University, China · School of Computer Science and Engineering, University of Electronic Science and Technology of China, China +1
DropVLA: An Action-Level Backdoor Attack on Vision-Language-Action Models
Oct 13, 2025Zonghuan Xu, Jiayu Li, Yunhan Zhao +3Adversarial Attacks on VLA Models Robotic Manipulation
Institute of Trustworthy Embodied AI, Fudan University, Shanghai, China. · Shanghai Key Laboratory of Multimodal Embodied AI, Shanghai, China. · City University of Hong Kong, Hong Kong SAR, China.
Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models
Oct 11, 2025Mingyang Lyu, Yinqian Sun, Erliang Lin +4Flow Matching Robotic Manipulation
Brain-inspired Cognitive AI Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China. · Beijing Institute of AI Safety and Governance, China. · University of Chinese Academy of Sciences (UCAS), Beijing, China. +3
Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered
Oct 9, 2025Jason Jabbour, Dong-Ki Kim, Max Smith +6Vision-Language-Action Models Efficient VLA Model Inference
FieldAI · Harvard University
Verifier-free Test-Time Sampling for Vision-Language-Action Models
Oct 7, 2025Suhyeok Jang, Dongyoung Kim, Changyeon Kim +2Test-Time Scaling Vision-Language-Action Models
1KAIST · 3RLWRLD · 2Seoul National University
MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning
Oct 3, 2025Tianyu Xu, Jiawei Chen, Jiazhao Zhang +5Robot Navigation Visual Robot Navigation
Peking University, Beijing, China · Galbot, Beijing, China · Shanghai Jiao Tong University, Shanghai, China +1
FailSafe: Reasoning and Recovery from Failures in Vision-Language-Action Models
Oct 2, 2025Zijun Lin, Jiafei Duan, Haoquan Fang +4Robot Failure Recovery Synthetic Data Generation for Robotics
1Nanyang Technological University · Centre for Frontier AI Research, A*STAR · 3Allen Institute for AI +1
EMMA: Enhancing Robotic Control Robustness through a Generative Visual Model
Sep 26, 2025Zhehao Dong, Xiaofeng Wang, Zheng Zhu +12Robustness of VLA Models Synthetic Data Generation for Robotics
GigaAI · Peking University · Tsinghua University +1
Ask-to-Clarify: Resolving Instruction Ambiguity through Multi-turn Dialogue
Sep 18, 2025Xingyao Lin, Xinghao Zhu, Tianyi Lu +6Language-Conditioned Robot Manipulation Vision-Language-Action Models
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China. · Shanghai Innovation Institute, Shanghai, China. · Mechanical Systems Control Lab, UC Berkeley, California, USA.
SpecPrune-VLA: Accelerating Vision-Language-Action Models via Action-Aware Self-Speculative Pruning
Sep 6, 2025Hanzhen Wang, Jiaming Xu, Yushun Xiang +4Efficient VLA Model Inference Vision-Language-Action Models
Shanghai Jiao Tong University · SII · Infinigence-AI
Confidence Calibration in Vision-Language-Action Models
Jul 23, 2025Thomas P Zollo, Richard ZemelUncertainty Estimation for VLA Models Vision-Language-Action Models
Columbia University
VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
Jul 7, 2025Juyi Lin, Amir Taherin, Arash Akbari +11Robotic Control Efficient VLA Model Inference
Northeastern University, Boston, USA · EmbodyX,San Mateo,USA

Feb 16, 2026J/K move · Enter open · S save

DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI

En Yu, Haoran Lv, Jianjian Sun, Kangheng Lin+45

Flow Matching for Action Generation Vision-Language-Action Models Embodied AI Robotics

Open paper AskPDF arXiv

Moving beyond the traditional paradigm of adapting internet-pretrained models to physical tasks, we present DM0, an Embodied-Native Vision-Language-Action (VLA) framework designed for Physical AI. Unlike approaches that treat physical grounding as a fine-tuning afterthought, DM0 unifies embodied manipulation and navigation by learning from heterogeneous data sources from the onset. Our methodology follows a comprehensive three-stage pipeline: Pretraining, Mid-Training, and Post-Training. First, we conduct large-scale unified pretraining on the Vision-Language Model (VLM) using diverse corpora--seamlessly integrating web text, autonomous driving scenarios, and embodied interaction logs-to jointly acquire semantic knowledge and physical priors. Subsequently, we build a flow-matching action expert atop the VLM. To reconcile high-level reasoning with low-level control, DM0 employs a hybrid training strategy: for embodied data, gradients from the action expert are not backpropagated to the VLM to preserve generalized representations, while the VLM remains trainable on non-embodied data. Furthermore, we introduce an Embodied Spatial Scaffolding strategy to construct spatial Chain-of-Thought (CoT) reasoning, effectively constraining the action solution space. Experiments on the RoboChallenge benchmark demonstrate that DM0 achieves state-of-the-art performance in both Specialist and Generalist settings on Table30.