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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HARP-VLA: Human-Robot Aligned Representation Learning for Vision-Language-Action Model
May 29, 2026Xiang Zhu, Puzhen Yuan, Yichen Liu +1Cross-Embodiment Robot Learning Robotic Manipulation
Institute for Interdisciplinary Information Sciences, Tsinghua University, China · Shanghai Qi Zhi Institute, China
Can Aerial VLA Models Cooperate? Evaluating Closed-Loop Air-Ground Coordination with CARLA-Air
May 29, 2026Tianle Zeng, Yanci Wen, Xueang Yu +1VLM Evaluation Aerial Robotics
Southern University of Science and Technology · Fudan University
Does Visual Information Play a Decisive Role in Vision-Language-Action Model Driving Behavior?
May 29, 2026Jingtao He, Hongliang Lu, Xiaoyun Qiu +2VLM Evaluation Autonomous Driving
Intelligent Transportation Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
Wall-OSS-0.5 Technical Report
May 29, 2026Ryan Yu, Pushi Zhang, Starrick Liu +24Zero-Shot Robotic Manipulation Cross-Embodiment Robot Learning
X Square Robot Team
Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring
May 29, 2026Seongheon Park, Wendi Li, Changdae Oh +4Robot Failure Detection Fault Detection
University of Wisconsin–Madison · Georgia Institute of Technology
Feat2Go: Visual Feature-Grounded Value Estimation for Embodied Reinforcement Learning
May 29, 2026Junyang Shu, Zhiwei Lin, Bingqing Wei +1Reward Shaping Value Function Estimation
Wangxuan Institute of Computer Technology, Peking University, China
Completion at the Boundary (CaB): Deployable Switching with Completion-Aware Control under Limited Calibration
May 29, 2026Yusuke Sano, Takeshi ItogaVision-Language-Action Models Embodied AI
Intelligent Systems Laboratory, SECOM Co., Ltd.
Primitive Subspaces Mediate Few-Shot Transfer in VLAs
May 29, 2026Anya Singh, Cabrel Happi, Jai Relan +2Robot Skill Learning Few-Shot Learning
Relling Systems
BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies
May 28, 2026Anya Singh, Cabrel Happi, Jai Relan +2Selective Prediction Conformal Prediction
Relling Systems
ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation
May 28, 2026Zeyuan He, Bowen Yang, Zhirui Fang +9Language-Conditioned Robot Manipulation Visuomotor Policy Learning
Torr Vision Group, University of Oxford · The Chinese University of Hong Kong, Shenzhen · Shanghai Jiao Tong University +3
Gaze2Act: Gaze-Conditioned Vision-Language-Action Policies for Interactive Robot Manipulation
May 28, 2026Kuangji Zuo, Gen Li, Bofan Lyu +9Language-Conditioned Robot Manipulation Robotic Manipulation
1MARS Lab, Nanyang Technological University
Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments
May 28, 2026Qiuyue Wang, Mingsheng Li, Jian Guan +37Language-Conditioned Robot Manipulation Robot Foundation Models
Qwen Team
BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models
May 28, 2026Zhongxi Chen, Yifan Han, Bin Qiu +8Contact-Rich Robotic Manipulation Robotic Manipulation
Shanghai Jiao Tong University (SJTU) · CASIA · Shanghai AI Laboratory +1
VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing
May 28, 2026Haoyuan Shi, Xiancong Ren, Yingji Zhang +9Vision-Language-Action Models
X-Humanoid · University of Science and Technology of China · University of Manchester +3
VisualThink-VLA: Visual Intermediate Reasoning for Effective and Low-Latency Vision-Language-Action Policies
May 28, 2026Mingjian Gao, Wenqiao Zhang, Yuqian Yuan +9Efficient VLM Inference Efficient VLA Model Inference
1Zhejiang University · 2Cornell University · 3National University of Singapore +1
PhAIL: A Real-Robot VLA Benchmark and Distributional Methodology
May 28, 2026Sergey ArkhangelskiyRobotic Policy Evaluation Vision-Language-Action Models
Positronic Robotics
SAFE-Pruner: Semantic Attention-Guided Future-Aware Token Pruning for Efficient Vision-Language-Action Manipulation
May 28, 2026Shilin Ma, Chubin Zhang, Changyuan Wang +6Efficient VLM Inference Robotic Manipulation
Tsinghua Shenzhen International Graduate School, Tsinghua University · GigaAI
VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models
May 28, 2026Dehao Huang, Aoxiang Gu, Chengjie Zhang +5Uncertainty Estimation for VLA Models Probability Calibration
Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China. · Zhongguancun Academy, Beijing, China · National Cybersecurity Academy, Wuhan University, Wuhan, China.
Mitigating State Aliasing in Vision-Language-Action Models via Inverse Dynamics Learning
May 28, 2026Kyujin Lee, Injae Kim, Jihwan Park +3Vision-Language-Action Models State Representation Learning
KAIST · Korea University
VLA-Pro: Cross-Task Procedural Memory Transfer for Vision-Language-Action Models
May 28, 2026Shengyu Si, Yuanzhuo Lu, Ruimeng Yang +3Generalization in Robotic Manipulation Robotic Manipulation
Institute of Trustworthy Embodied AI, Fudan University · 2Shanghai Key Laboratory of Multimodal Embodied AI · 3Shanghai Xinzhi Embodied Intelligence Technology Co., Ltd.
ElegantVLA: Learning When to Think for Efficient Vision-Language-Action Models
May 28, 2026Ye Li, Huanan Liu, Kangye Ji +7Efficient VLM Inference Efficient VLA Model Inference
Tsinghua University · University of Illinois at Urbana-Champaign
3DVLA: Enhancing Vision-Language-Action Models via 3D Spatial and Instance Understanding
May 28, 2026Zhongyu Xia, Yousen Tang, Bingqing Wei +13D Spatial Reasoning Robotic Manipulation
Wangxuan Institute of Computer Technology, Peking University · This work was done as an intern at PKU.
ReasonBreak: Probing Vulnerabilities in Reasoning-Enabled Vision-Language-Action Models for Autonomous Driving
May 27, 2026Mohammadreza Teymoorianfard, Jean-Philippe Monteuuis, Jonathan Petit +1VLMs for Autonomous Driving Autonomous Driving Safety Evaluation
University of Massachusetts Amherst · Qualcomm
Ω-QVLA: Robust Quantization for Vision-Language-Action Models via Composite Rotation and Per-step Scaling
May 27, 2026Xinyu Wang, Mingze Li, Sicheng Lyu +6VLM Quantization Efficient VLA Model Inference
McGill University · Université de Montréal · Mila – Quebec AI Institute +2
How VLAs Fail Differently: Black-Box Action Monitoring Reveals Architecture-Specific Failure Signatures
May 27, 2026Krishnam GuptaAgent Failure Analysis Robot Failure Detection
Independent Research San Francisco, CA
PrimitiveVLA: Learning Reusable Motion Primitives for Efficient and Generalizable Robotic Manipulation
May 27, 2026Yutai Li, Shaohui Peng, Jiaming Guo +8Long-Horizon Robotic Manipulation Robot Skill Learning
State Key Lab of Processors, Institute of Computing Technology, CAS · Jiangsu Key Laboratory of AI for Industries, Institute of AI for Industries, CAS · University of Chinese Academy of Sciences +3
GEM: Generative Supervision Helps Embodied Intelligence
May 27, 2026Ruowen Zhao, Bangguo Li, Zuyan Liu +9Vision-Language-Action Models Embodied AI
Tsinghua University · Tencent Hunyuan
What Frozen VLAs Already Know About Success: A Probing Study of Value-Like Structure in Foundation Robot Policies
May 27, 2026Jiachen Zhang, Junnan Nie, Junyi Lao +4Value Function Estimation Robotic Manipulation
Peking University · China Agricultural University · 1Peking University. +1
Mag-VLA: Vision-Language-Action Model for Bimanual Magnetically Actuated Microrobot Manipulation
May 27, 2026Yongchen Wang, Kangyi Lu, Lan Wei +1Language-Conditioned Robot Manipulation Bimanual Robotic Manipulation
Department of Bioengineering, Imperial-X, Imperial College London
ProgVLA: Progress-Aware Robot Manipulation Skill Learning
May 27, 2026Seungsu Kim, Jinyoung Choi, Seungmin Baek +1Cross-Modal Learning Efficient Multimodal Inference
NAVER LABS Europe · NAVER LABS

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HARP-VLA: Human-Robot Aligned Representation Learning for Vision-Language-Action Model

Xiang Zhu, Puzhen Yuan, Yichen Liu, Jianyu Chen

Institute for Interdisciplinary Information Sciences, Tsinghua University, China · Shanghai Qi Zhi Institute, China

Cross-Embodiment Robot Learning Robotic Manipulation Vision-Language-Action Models Vision-Language Alignment

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Learning generalizable vision-language-action (VLA) models from large-scale human videos is promising but challenging due to cross-embodiment discrepancies in both visual observations and executable actions. While latent action models reduce the action execution gap by learning action abstractions, they still rely on visual features. Thus, misaligned human and robot visual representations can lead to inconsistencies in policy inputs and induce domain-dependent latent actions, hindering effective co-training with human videos.To address this, we propose HARP, a human-robot aligned representation learning framework for more effective VLA pretraining from human videos. Specifically, HARP uses paired human-robot demonstrations as cross-embodiment bridges and abundant unpaired human and robot videos as a scalable dynamics supervision data source. It trains a robot-adapted visual encoder and a latent action model with manipulation-centric auxiliary cues and a source-relative pair-discriminative alignment loss, which adapts robot representations toward human semantics while preserving pair-level discrimination. The learned aligned vision encoder and latent action model provide a unified vision and action representation for VLA-style policy learning, where human and robot videos provide vision-language-to-latent-action supervision and a lightweight robot action head grounds latent actions into executable commands. Experiments on feature visualization, simulation, and real-world manipulation show improved human-robot alignment and downstream policy performance, achieving 4.481 average length on CALVIN ABC

\rightarrow

D and a 7.1 percentage-point improvement in real-world success rate over the strongest evaluated baseline. Code, videos, and demos are available at https://puzhenyuan.github.io/HARP-VLA-website/.