Vision-Language-Action Models

Also known as VLA

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Latest papers 1,140

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

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How VLAs (Really) Work In Open-World Environments
Apr 23, 2026Amir Rasouli, Yangzheng Wu, Zhiyuan Li +4VLM Robustness Long-Horizon Robotic Manipulation
Noahs Ark Laboratory, Huawei Technologies Canada. · while at Huawei Canada.
PokeVLA: Empowering Pocket-Sized Vision-Language-Action Model with Comprehensive World Knowledge Guidance
Apr 22, 2026Yupeng Zheng, Xiang Li, Songen Gu +12Robot Foundation Models Efficient VLA Models
CASIA · TARS Robotics · Tsinghua University +3
Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models
Apr 22, 2026Shelly Francis-Meretzki, Mirco Mutti, Yaniv Romano +1Uncertainty Estimation for VLA Models Vision-Language-Action Models
1Technion - Israel Institute of Technology.
A Vision-Language-Action Model for Adaptive Ultrasound-Guided Needle Insertion and Needle Tracking
Apr 22, 2026Yuelin Zhang, Qingpeng Ding, Longxiang Tang +2Robotic Insertion Robotic Ultrasound
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong · Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong · Shenzhen International Graduate School, Tsinghua University, China +1
EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training
Apr 21, 2026Yiyang Du, Zhanqiu Guo, Xin Ye +2VLM Adaptation Vision-Language-Action Models
Language Technologies Institute, Carnegie Mellon University · Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI)
VLA Foundry: A Unified Framework for Training Vision-Language-Action Models
Apr 21, 2026Jean Mercat, Sedrick Keh, Kushal Arora +5Robotic Manipulation Vision-Language-Action Models
Toyota Research Institute
SpanVLA: Learning from Negative-Recovery Samples with Fast Action Bridging for Vision-Language-Action Model
Apr 21, 2026Zewei Zhou, Ruining Yang, Xuewei Qi +7VLMs for Autonomous Driving Autonomous Driving
Tony · University of California, Los Angeles, USA · Motional, USA +1
HELM: Harness-Enhanced Long-horizon Memory for Vision-Language-Action Manipulation
Apr 20, 2026Zijian Zeng, Fei Ding, Huiming Yang +1Long-Horizon Robotic Manipulation Robot Failure Recovery
Tsinghua University · Alibaba Group · Bengbu University
Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
Apr 20, 2026Jinghui Lu, Jiayi Guan, Zhijian Huang +47Trajectory Generation VLMs for Autonomous Driving
Test-Time Perturbation Learning with Delayed Feedback for Vision-Language-Action Models
Apr 20, 2026Zehua Zang, Xi Wang, Fuchun Sun +4Test-Time Adaptation Test-Time Adaptation of VLMs
Institute of Software, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Tsinghua University, Beijing, China +2
Unmasking the Illusion of Embodied Reasoning in Vision-Language-Action Models
Apr 20, 2026Haiweng Xu, Sipeng Zheng, Hao Luo +3VLM Evaluation VLM Reasoning
1Peking University · 3BeingBeyond · 2Tsinghua University
OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models
Apr 20, 2026Yiwei Zhang, Xuesong Chen, Jin Gao +5VLMs for Autonomous Driving End-to-End Autonomous Driving
State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA · School of Artificial Intelligence, University of Chinese Academy of Sciences · AutoLab, School of Artificial Intelligence, Shanghai Jiao Tong University +2
Can Explicit Physical Feasibility Benefit VLA Learning? An Empirical Study
Apr 20, 2026Yubai Wei, Chen Wu, Hashem HaghbayanVision-Language-Action Models Robot Imitation Learning
Department of Computing, University of Turku, Finland
ST- $π$ : Structured SpatioTemporal VLA for Robotic Manipulation
Apr 20, 2026Chuanhao Ma, Hanyu Zhou, Shihan Peng +3Language-Conditioned Robot Manipulation Robotics
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology · School of Computing, National University of Singapore · School of Computing, Macquarie University
OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation
Apr 20, 2026Kuanning Wang, Ke Fan, Chenhao Qiu +5VLM Robustness Flow Matching
Fudan University Shanghai, China · University of Southern California Los Angeles, CA, USA · KAUST Saudi Arabia
ReFineVLA: Multimodal Reasoning-Aware Generalist Robotic Policies via Teacher-Guided Fine-Tuning
Apr 20, 2026Tuan Van Vo, Tan Q. Nguyen, Khang Nguyen +5Robotic Manipulation VLM Reasoning
VinRobotics, Hanoi. Vietnam · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), UAE. · University of Texas at Arlington, Texas, USA +4
AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models
Apr 20, 2026Tingzheng Jia, Kan Guo, Lanping Qian +6Robot Policy Learning Language-Conditioned Robot Manipulation
Beijing University of Technology Beijing, China · Beihang University Beijing, China · Nanjing University of Science and Technology Nanjing, China
OmniVLA-RL: A Vision-Language-Action Model with Spatial Understanding and Online RL
Apr 20, 2026Haoxiang Jie, Yaoyuan Yan, Xiangyu Wei +4Flow Matching Policy Optimization
1AI Lab, Country Garden Services · 3VBot · 2Omni AI +1
Infrastructure-Centric World Models: Bridging Temporal Depth and Spatial Breadth for Roadside Perception
Apr 19, 2026Siyuan Meng, Chengbo AiWorld Model Learning World Models
Department of Civil and Environmental Engineering, University of Massachusetts Amherst
ReconVLA: An Uncertainty-Guided and Failure-Aware Vision-Language-Action Framework for Robotic Control
Apr 17, 2026Lingling Chen, Zongyao Lyu, William J. BeksiUncertainty Estimation for VLA Models Robot Failure Detection
Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, USA
AEGIS: Anchor-Enforced Gradient Isolation for Knowledge-Preserving Vision-Language-Action Fine-Tuning
Apr 17, 2026Guransh SinghVLM Adaptation Robotic Manipulation
Independent Researcher
Foundation Models in Robotics: A Comprehensive Review of Methods, Models, Datasets, Challenges and Future Research Directions
Apr 16, 2026Aggelos Psiris, Vasileios Argyriou, Evangelos K. Markakis +4Large Language Model-Based Robot Planning Robot Foundation Models
Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece · Department of Networks and Digital Media, Kingston University, London, United Kingdom · Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece +4
World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems
Apr 16, 2026Runze Li, Hongyin Zhang, Junxi Jin +5Vision-Language-Action Models Model-Based Planning
Westlake University Hangzhou, China · Nanjing University Suzhou Campus, 1520 Taihu Avenue, Suzhou, China
HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System
Apr 15, 2026Tianshuo Yang, Guanyu Chen, Yutian Chen +8Robotic Manipulation Robotic Control
The University of Hong Kong · Shanghai AI Laboratory · Shanghai Jiao Tong University +1
Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents
Apr 15, 2026Angelo Moroncelli, Roberto Zanetti, Marco Maccarini +1RL for Robotics Data-Efficient Robot Learning
University of Applied Science and Arts of Southern Switzerland, Department of Innovative Technologies, IDSIA-SUPSI, Lugano, Switzerland · Università della Svizzera Italiana, Faculty of Informatics, Lugano, Switzerland · Politecnico di Milano, Mechanical Department, Milano, Italy
Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
Apr 15, 2026Hanxuan Chen, Jie Zheng, Siqi Yang +9Vision-Language Navigation Aerial VLN
Autel Robotics, Shenzhen, China · School of Intelligent Software and Engineering, Nanjing University, Nanjing, China · School of Computer, Data & Information Sciences, University of Wisconsin-Madison, Madison, WI, USA +3
HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models
Apr 14, 2026Zixing Chen, Yifeng Gao, Li Wang +8Robotics AI Safety Evaluation
Fudan University · City University of Hong Kong · Shanghai Innovation Institute
StarVLA- $α$ : Reducing Complexity in Vision-Language-Action Systems
Apr 13, 2026Jinhui Ye, Ning Gao, Senqiao Yang +7Robot Foundation Models Robotic Manipulation
HKUST · XJTU · CUHK +3
LARY: A Latent Action Representation Yielding Benchmark for Generalizable Vision-to-Action Alignment
Apr 13, 2026Dujun Nie, Fengjiao Chen, Qi Lv +4Latent Action Learning Vision-Language-Action Models
Meituan, Beijing, China
DA-PTQ: Drift-Aware Post-Training Quantization for Efficient Vision-Language-Action Models
Apr 13, 2026Siyuan Xu, Tianshi Wang, Fengling Li +2Post-Training Quantization Efficient VLA Models
Tongji University Shanghai, China · University of Technology Sydney Sydney, Australia

Apr 23, 2026J/K move · Enter open · S save

How VLAs (Really) Work In Open-World Environments

Amir Rasouli, Yangzheng Wu, Zhiyuan Li, Rui Heng Yang+3

Noahs Ark Laboratory, Huawei Technologies Canada. · while at Huawei Canada.

VLM Robustness Long-Horizon Robotic Manipulation AI Agent Benchmarks AI Safety Evaluation Vision-Language-Action Models Robot Manipulation Benchmarks+2

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Vision-language-action models (VLAs) have been extensively used in robotics applications, achieving great success in various manipulation problems. More recently, VLAs have been used in long-horizon tasks and evaluated on benchmarks, such as BEHAVIOR1K (B1K), for solving complex household chores. The common metric for measuring progress in such benchmarks is success rate or partial score based on satisfaction of progress-agnostic criteria, meaning only the final states of the objects are considered, regardless of the events that lead to such states. In this paper, we argue that using such evaluation protocols say little about safety aspects of operation and can potentially exaggerate reported performance, undermining core challenges for future real-world deployment. To this end, we conduct a thorough analysis of state-of-the-art models on the B1K Challenge and evaluate policies in terms of robustness via reproducibility and consistency of performance, safety aspects of policies operations, task awareness, and key elements leading to the incompletion of tasks. We then propose evaluation protocols to capture safety violations to better measure the true performance of the policies in more complex and interactive scenarios. At the end, we discuss the limitations of the existing VLAs and motivate future research.