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

1,154 papers249 papers in the last four weeks, up 144% on the four weeks before. 1.5% of all new papers.

ActiveFly-Bench: Aligning Embodied Question Answering with Vision-Language-Action for Aerial Embodied Perception
Jul 11, 2026Weichen Zhang, Shiquan Yu, Yinan Zhu +9Active Perception Aerial Robotics
1Tsinghua University · 2Manifold AI
On the Efficiency of LoRA Fine-Tuning for Vision-Language-Action Models in Industrial Robotic Manipulation
Jul 11, 2026Finn Ferchau, Daniel Pommer, Cristian AxenieLow-Rank Adaptation Vision-Language-Action Models
Technische Hochschule Nürnberg Georg Simon Ohm, Nürnberg, Germany · Siemens AG, München, Germany · Fraunhofer Institute for Integrated Circuits (IIS), Erlangen, Germany
TS-Mask VLA: 2D Temporal-Spatial Masking for Vision-Language-Action Model with Effective Bridging
Jul 10, 2026Shengzhuo Yang, Ronghao Yu, Chuanjie Lv +5Robot Motion Generation Robotic Manipulation
Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China
Learning from Hindsight for VLA Reinforcement Learning
Jul 10, 2026Iris Xu, Sunshine Jiang, John Marangola +2Vision-Language-Action Models RL for VLMs
Massachusetts Institute of Technology · MIT-IBM Computing Research Lab
CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding
Jul 9, 2026Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya +3VLM Adaptation Robotic Manipulation
Ochanomizu University · OMRON SINIC X Corporation · University of Tokyo +1
Native Video-Action Pretraining for Generalizable Robot Control
Jul 9, 2026Qihang Zhang, Lin Li, Luyao Zhang +26Robotic Control Language-Conditioned Robot Manipulation
FabriVLA: A Lightweight Vision-Language-Action Model for Precise Multi-Task Manipulation
Jul 9, 2026Shiyuan Yang, Borong Zhang, Jizheng Zhang +5Efficient VLA Models Robotic Manipulation
University of Macau · 2Mese Technology Limited Co., Ltd. · 3FabriX team at Youibot Robotics Co., Ltd.
Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents
Jul 9, 2026Yixian Zhang, Huanming Zhang, Feng Gao +14Long-Horizon Robotic Manipulation Language-Conditioned Robot Manipulation
Tsinghua University · Striding AI · Purdue University +4
WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
Jul 9, 2026Xuerun Yan, Zhexi Lian, Nuoheng Zhang +5End-to-End Autonomous Driving World Models
Tongji University, China · Nanyang Technological University, Singapore
TFP: Temporally Conditioned Memory-Fusion Policies for Visuomotor Learning
Jul 9, 2026Yushen Liang, Yue Peng, Baosheng Jin +6Memory-Augmented VLMs Visuomotor Policy Learning
NYU Shanghai, Shanghai, China · University of Electronic Science and Technology of China, Chengdu, China
LEEVLA: Seeing What Matters in Latent Environment Evolution for Vision-Language-Action
Jul 9, 2026Qi Lyu, Baicheng Liu, Xudong Wang +3Latent Dynamics Modeling Vision-Language-Action Models
State Key Laboratory of Robotics and Intelligent Systems · Shenyang Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences +1
Dual Latent Memory in Vision-Language-Action Models for Robotic Manipulation
Jul 8, 2026Hongyu Qu, Jianzhe Gao, Xiaobin Hu +6Memory-Augmented VLMs Robotic Manipulation
Nanjing University of Science and Technology · Zhejiang University · National University of Singapore
HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation
Jul 8, 2026Shaopeng Zhai, Qi Zhang, Tianyi Zhang +5Robot Teleoperation Human-in-the-Loop AI
Continuous Learning Team, Shanghai AI Laboratory
Vision Language Action (VLA) Models for Unmanned Aerial Robotics and Bimanual Manipulation: A Review
Jul 7, 2026Inkyu Sa, Chanoh Park, Hea-Min Lee +2Aerial Robotics Bimanual Robotic Manipulation
Chef Robotics, San Francisco, CA 94103, USA · RovifyLab, Gyeonggi 13840, Republic of Korea · IT Application Research Center, Jeonbuk Regional Branch, Korea Electronics Technology Institute (KETI), Jeonju 54853, Republic of Korea +1
NativeMEM: Native Memory Compression for Long-Horizon Robotic Manipulation
Jul 7, 2026Ziye Wang, Modi Shi, Chaojun Ni +5Memory-Augmented VLMs Long-Horizon Robotic Manipulation
The University of Hong Kong · Beihang University · Peking University +2
Lift3D-VLA: Lifting VLA Models to 3D Geometry and Dynamics-Aware Manipulation
Jul 7, 2026Jiaming Liu, Qingpo Wuwu, Nuowei Han +8Robotic Manipulation Vision-Language-Action Models
State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China · CUHK, Shatin, Hong Kong · AI2Robotics, Beijing, China
Pelican-VLA 0.5: Attending Before Acting Benefits Generalization
Jul 7, 2026Zeyuan Ding, Wenhai Liu, Yang Xu +6Next-Action Prediction Generalization in Robotic Manipulation
Beijing Innovation Center of Humanoid Robotics (X-Humanoid)
SIEVE: Structure-Aware Data Selection for Imitation Learning with VLA Models
Jul 7, 2026Changti Wu, Bin Yu, Zhaolong Shen +6Imitation Learning Vision-Language-Action Models
1East China Normal University · 2Zhongguancun Academy · 3Harbin Institute of Technology +5
From Foundation to Application: Improving VLA Models in Practice
Jul 7, 2026Wei Wu, Fangjing Wang, Fan Lu +21Long-Horizon Robotic Manipulation World Models for Robotics
Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement
Jul 7, 2026Ryuji Oi, Hikari Otsuka, Kosuke Matsushima +4Efficient VLA Model Inference Vision-Language-Action Models
Institute of Science Tokyo
Diagnosing Semantic Handoff Failures in Agent-Orchestrated Vision-Language-Action Skill Composition
Jul 7, 2026Ke Rui, Yushen Zuo, Jiawei Wang +4Long-Horizon Robotic Manipulation Robotics
SimpleAI
From Fixed to Free Cameras: Calibration-Free View-Robust Vision-Language-Action Model
Jul 6, 2026Wenhao Li, Xueying Jiang, Quanhao Qian +4Language-Conditioned Robot Manipulation Hand-Eye Calibration
1Nanyang Technological University · 2DAMO Academy, Alibaba Group · 3HuPan Lab +1
Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation
Jul 6, 2026Jiaqi Peng, Xiqian Yu, Delin Feng +10Long-Horizon Robotic Manipulation Language-Conditioned Robot Manipulation
Tsinghua University · Shanghai AI Laboratory · Peking University +1
Green for Go, Red for No: Visual Grounding via Semantic Segmentation for VLA Navigation Policies
Jul 6, 2026Adrian Szvoren, Dimitrios Kanoulas, Nilufer TuptukVisual Navigation Vision-Language Navigation
Department of Computer Science University College London · Department of Security and Crime Science University College London
InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization
Jul 6, 2026Haoxiang Ma, Junhao Cai, Xiaoxu Xu +26Generalization in Robotic Manipulation Visuomotor Policy Learning
Physical Intelligence Team, Shanghai AI Laboratory
DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation
Jul 6, 2026Jian Zhu, Jianjun Zhang, Taiyi Su +10Language-Conditioned Robot Manipulation Robot Foundation Models
1AIRC, Midea Group · 2Tongji University
CAC-VLA: Context-Gated Action Conditioning for Vision-Language-Action Models
Jul 6, 2026Yifu Xiong, Wenhao Yu, Jiaxuan Lin +5Latent Action Learning Language-Conditioned Robot Manipulation
University of Science and Technology of China (USTC) · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center
Do Vision-Language-Action Models Mean What They Say? On the Role of Faithfulness in Embodied Reasoning
Jul 6, 2026Matthew Foutter, Matteo Cercola, Lena Wild +4VLMs for Autonomous Driving Vision-Language-Action Models
Stanford University · Politecnico di Milano · KTH Royal Institute of Technology +2
PixelPilot: Scalable Vision-Language-Action Models for End-to-End Autonomous Driving
Jul 6, 2026Pin Tang, Guoqing Wang, Xiangxuan Ren +4End-to-End Autonomous Driving Vision-Language-Action Models
MoE Key Lab of Artificial Intelligence, Institute of AI, Shanghai Jiao Tong University, Shanghai, China · Central Research Institute, Huawei, Beijing, China
SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies
Jul 6, 2026Dijia Zhan, Xuemiao Xu, Jinyi Li +1Vision-Language-Action Models Action Chunking
South China University of Technology

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

ActiveFly-Bench: Aligning Embodied Question Answering with Vision-Language-Action for Aerial Embodied Perception

Weichen Zhang, Shiquan Yu, Yinan Zhu, Peizhi Tang+8

1Tsinghua University · 2Manifold AI

Active Perception Aerial Robotics Embodied QA Vision-Language-Action Models

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We introduce ActiveFly-Bench, the first benchmark to bridge cyberspace reasoning and physical-world interaction for UAV embodied perception. The benchmark decomposes active perception into three hierarchical tasks: Aerial Embodied Question Answering (Air-EQA), Observation Behavior Planning (OBP), and Fine-grained Language-guided UAV Control (FLUC), explicitly connecting high-level task understanding, behavior planning, and low-level control. The datasets are collected from both real-world and simulated outdoor environments for training and evaluation. We further develop ActiveFly, a closed-loop UAV agent that integrates visual-language reasoning with fine-grained control, and deploy it on a physical UAV platform. Experiments with representative VLMs and VLA models show that current UAV agents still struggle with behavior planning, viewpoint adjustment, and robust task completion in active perception. These results establish ActiveFly-Bench as a new testbed for embodied aerial intelligence.