Vision-Language-Action Models

Also known as VLA

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249 papers in the last four weeks, up 144% on the four weeks before. 1.5% of all new papers.

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CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving
May 11, 2026Minqing Huang, Yujiao Xiang, Zihan Liang +7VLMs for Autonomous Driving Diffusion-Based Planning
Afari Intelligent Drive · University of Electronic Science and Technology of China · Shanghai Jiao Tong University +2
Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs
May 11, 2026Jianchao Zhao, Huoren Yang, Yusong Hu +6Continual Test-Time Adaptation Robotic Manipulation
College of Artificial Intelligence, Xi’an Jiaotong University · 2One Robotics · 3Shenzhen University of Advanced Technology
LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models
May 11, 2026Boyang Shen, Kaixiang Yang, Hao Wang +4Efficient VLM Inference Efficient VLA Model Inference
1Huazhong University of Science and Technology · 2Wuhan United Imaging Surgical Co.,Ltd. (UIS)
SABER: A Scalable Action-Based Embodied Dataset for Real-World VLA Adaptation
May 10, 2026Narsimha Menga, Parikshit Sakurikar, Amirreza Rouhi +6Robot Skill Learning Robotic Data Collection
DreamVu1
RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models
May 10, 2026Weijia Liufu, Xiaoyu Guo, Ruiyi Chen +16VLM Robustness Robot Failure Recovery
1Sun Yat-sen University · 2South China University of Technology · 3Peng Cheng Laboratory +3
Towards Backdoor-Based Ownership Verification for Vision-Language-Action Models
May 9, 2026Ming Sun, Rui Wang, Xingrui Yu +5Vision-Language-Action Models
Institute of Information Engineering, Chinese Academy of Sciences, China · School of Cyber Security, University of Chinese Academy of Sciences, China · A*STAR Institute of High Performance Computing (A*STAR IHPC), Singapore +5
ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models
May 9, 2026Yanbin Hu, Jin Cui, Jiayi Lu +6Memory-Augmented VLMs Long-Horizon Robotic Manipulation
School of Software, Xi’an Jiaotong University · State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · School of Artificial Intelligence, Xi’an Jiaotong University
Preserving Foundational Capabilities in Flow-Matching VLAs through Conservative SFT
May 9, 2026Tianyi Zhang, Shaopeng Zhai, Haoran Zhang +2Supervised Fine-Tuning Continual Learning for VLA Models
Shanghai Artificial Intelligence Laboratory
VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving
May 9, 2026Rui Zhao, Jianlin Yu, Zhenhai Gao +2VLMs for Autonomous Driving End-to-End Autonomous Driving
College of Automotive Engineering, Jilin University · 2The National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University · 3ReeFocus AI Technology
Geometry Guided Self-Consistency for Physical AI
May 9, 2026Yinwei Dai, Zhuofu Chen, Lijie Yang +1Diffusion Policy Vision-Language-Action Models
Princeton University
ATAAT: Adaptive Threat-Aware Adversarial Tuning Framework against Backdoor Attacks on Vision-Language-Action Models
May 9, 2026Kewei Chen, Yayu Long, Shuai Li +1Adversarial Attacks Adversarial Attacks on VLA Models
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
Failing Forward: Adaptive Failure-Informed Learning for Vision-Language-Action Models
May 8, 2026Meng Zheng, Samhita Marri, Anwesa Choudhuri +6Diffusion Policy Robotic Manipulation
United Imaging Intelligence, Boston, MA, USA · University of Illinois Urbana-Champaign, Urbana, IL, USA
One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy
May 8, 2026Zuojin Tang, Shengchao Yuan, Xiaoxin Bai +4Action-Conditioned World Models Efficient VLA Models
Zhejiang University · Central South University · Harbin Institute of Technology +2
ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
May 8, 2026Yuhao Zhou, Yunpeng Zhu, Yang Zhou +7Vision-Language Models Efficient VLA Models
Sichuan University · Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education · Zhejiang University +2
AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models
May 8, 2026Xiaoqi Li, Muhe Cai, Jiadong Xu +5Contact-Rich Robotic Manipulation VLM Adaptation
School of Computer Science, Peking University · PrimeBot · PKU Lab
BioProVLA-Agent: An Affordable, Protocol-Driven, Vision-Enhanced VLA-Enabled Embodied Multi-Agent System with Closed-Loop-Capable Reasoning for Biological Laboratory Manipulation
May 8, 2026Zhaohui Du, Zhe Wang, Dongzhan Zhou +9Multi-Agent Orchestration Long-Horizon Robotic Manipulation
Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education, East China University of Science and Technology, Shanghai, CN. · Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, CN. · AI for Science Center, Shanghai AI Laboratory, Shanghai, CN. +2
Sword: Style-Robust World Models as Simulators via Dynamic Latent Bootstrapping for VLA Policy Post-Training
May 8, 2026Jiaxuan Gao, Yongjian Guo, Zhong Guan +5World Model Learning World Models
Tianjin University · Tsinghua University · Swinburne University of Technology +1
OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
May 7, 2026Yushan Liu, Peibo Sun, Shoujie Li +7World Models for Robotics Action-Conditioned World Models
Tsinghua University · Shanghai Jiao Tong University · Nanyang Technological University
VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts
May 7, 2026Yuhua Jiang, Junjie Lu, Xinyao Qin +4Fine-Tuning Vision-Language-Action Models
Microsoft Research Asia · Tsinghua University
TriRelVLA: Triadic Relational Structure for Generalizable Embodied Manipulation
May 7, 2026Hanyu Zhou, Chuanhao Ma, Gim Hee LeeLanguage-Conditioned Robot Manipulation Robotic Manipulation
School of Computing, National University of Singapore · School of Artificial Intelligence and Automation, Huazhong University of Science and Technology
ConsisVLA-4D: Advancing Spatiotemporal Consistency in Efficient 3D-Perception and 4D-Reasoning for Robotic Manipulation
May 6, 2026Wei Li, Jizhihui Liu, Li Yixing +3Multi-View Consistency Spatiotemporal Reasoning
Harbin Institute of Technology, Shenzhen · State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE, China · Shenzhen Loop Area Institute
Test-Time Training for Visual Foresight Vision-Language-Action Models
May 6, 2026Sangwu Park, Wonjoong Kim, Yeonjun In +3VLM Robustness Test-Time Training
KAIST, Seoul, South Korea.
From Pixels to Tokens: A Systematic Study of Latent Action Supervision for Vision-Language-Action Models
May 6, 2026Yihan Lin, Haoyang Li, Yang Li +4Latent Action Learning Vision-Language-Action Models
School of Information, Renmin University of China, Beijing, China · Key Laboratory of Data Engineering and Knowledge Engineering, Beijing, China · Engineering Research Center of Database and Business Intelligence, Beijing, China
RLDX-1 Technical Report
May 5, 2026Dongyoung Kim, Huiwon Jang, Myungkyu Koo +65Contact-Rich Robotic Manipulation Robotic Manipulation
Understanding Asynchronous Inference Methods for Vision-Language-Action Models
May 4, 2026Ayoub AgouzoulRobotic Control Efficient VLM Inference
École polytechnique · Systems Group, ETH Zürich
MolmoAct2: Action Reasoning Models for Real-world Deployment
May 4, 2026Haoquan Fang, Jiafei Duan, Donovan Clay +26Robotics Efficient VLA Models
1Allen Institute for AI · University of Washington · 3National University of Singapore +6
Seeing Realism from Simulation: Efficient Video Transfer for Vision-Language-Action Data Augmentation
May 4, 2026Chenyu Hui, Xiaodi Huang, Siyu Xu +5Synthetic Data Augmentation Video Diffusion Models
Shanghai Jiao Tong University · Xi’an Jiaotong University · Institute of Automation, Chinese Academy of Sciences +3
Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference
May 4, 2026Yudong Liu, Yuan Li, Zijia Tang +12Efficient VLM Inference Efficient VLA Model Inference
1Duke University · 2Qualcomm AI Research · University of Florida
VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation
May 3, 2026Zijian An, Hadi Khezam, Bill Cai +5Robotic Manipulation Vision-Language-Action Models
Drexel University, Philadelphia, PA 19104, USA · Virginia Seafood Agricultural Research and Extension Center, Virginia Tech, 15 Rudd Ln, Hampton, VA 23669, USA · Amazon Store Foundation AI (SFAI), 12 W 39th St, New York, NY 10018, USA
Phone2Act: A Low-Cost, Hardware-Agnostic Teleoperation System for Scalable VLA Data Collection
May 3, 2026Om Mandhane, Bipin Yadav, Sangeetha Prasanna Ram +1Robotic Data Collection Robot Teleoperation
Dept. of Automation & Robotics Engineering, Vivekanand Education Society’s Institute of Technology (VESIT), Mumbai, India.

May 11, 2026J/K move · Enter open · S save

CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving

Minqing Huang, Yujiao Xiang, Zihan Liang, Jiajie Huang+6

Afari Intelligent Drive · University of Electronic Science and Technology of China · Shanghai Jiao Tong University+2

VLMs for Autonomous Driving Diffusion-Based Planning World Models Vision-Language-Action Models World Models for Autonomous Driving Autonomous Driving Planning

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Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing reasoning mechanisms still struggle to provide planning-oriented intermediate representations: textual Chain-of-Thought (CoT) fails to preserve continuous spatiotemporal structure, while latent world reasoning remains difficult to use as a direct condition for action generation. In this paper, we propose CoWorld-VLA, a multi-expert world reasoning framework for autonomous driving, where world representations serve as explicit conditions to guide action planning. CoWorld-VLA extracts complementary world information through multi-source supervision and encodes it into expert tokens within the VLA, thereby providing planner-accessible conditioning signals. Specifically, we construct four types of tokens: semantic interaction, geometric structure, dynamic evolution, and ego trajectory tokens, which respectively model interaction intent, spatial structure, future temporal dynamics, and behavioral goals. During action generation, CoWorld-VLA employs a diffusion-based hierarchical multi-expert fusion planner, which is coupled with scene context throughout the joint denoising process to generate continuous ego trajectories. Experiments show that CoWorld-VLA achieves competitive results in both future scene generation and planning on the NAVSIM v1 benchmark, demonstrating strong performance in collision avoidance and trajectory accuracy. Ablation studies further validate the complementarity of expert tokens and their effectiveness as planning conditions for action generation. Code will be available at https://github.com/AFARI-Research/CoWorld-VLA.