Robustness of VLA Models

VLA: Vision-Language-Action

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35 papers in the last four weeks, up 338% on the four weeks before. 0.3% of all new papers.

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StableVLA: Towards Robust Vision-Language-Action Models without Extra Data
May 18, 2026Yiyang Fu, Chubin Zhang, Shukai Gong +7Image Corruption Robustness Vision-Language-Action Models
Peking University · Tsinghua University · Astribot +2
Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation in Autonomous Driving Models
May 17, 2026Nicanor Mayumu, Xiaoheng Deng, Patrick MukalaCoT Faithfulness VLMs for Autonomous Driving
School of Computer Science and Engineering Central South University 932 Lushan Road, Changsha, Hunan P. R. China · School of Computer Science University of Wollongong in Dubai UOWD Building, Dubai Knowledge Park Dubai, United Arab Emirates
VLAMotor: Test-Guided Enhancement of Vision-Language-Action Models via Agent-BasedData Synthesis
May 16, 2026Zeqin Liao, Peifan Ren, Zixu Gao +6VLM Adaptation Synthetic Data Generation for Robotics
School of computing and data science, Nanyang Technological University · School of Software Engineering, Sun Yat-sen University, China and GuangDong Engineering Technology Research Center of Blockchain, China · Northwest A&F University
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
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
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.
Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms
Apr 26, 2026Qi Li, Bo Yin, Weiqi Huang +6AI Agent Safety Adversarial Attacks on VLA Models
National University of Singapore · Peking University · Monash University
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.
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
LIBERO-Para: A Diagnostic Benchmark and Metrics for Paraphrase Robustness in VLA Models
Mar 30, 2026Chanyoung Kim, Minwoo Kim, Minseok Kang +2Vision-Language Grounding Vision-Language-Action Models
Soongsil University · Chung-Ang University
StageCraft: Execution Aware Mitigation of Distractor and Obstruction Failures in VLA Models
Mar 21, 2026Kartikay Milind Pangaonkar, Prabin Rath, Omkar Patil +1VLM Robustness Robotic Manipulation
Arizona State University
Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration
Mar 6, 2026Ninghao Zhang, Bin Zhu, Shijie Zhou +1Vision-Language-Action Models Robustness of VLA Models
Tsinghua University · Singapore Management University · Institute of Trustworthy Embodied AI, Fudan University +1
When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAs
Feb 19, 2026Yu Fang, Yuchun Feng, Dong Jing +5VLM Robustness Counterfactual Evaluation of VLMs
University of North Carolina at Chapel Hill
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
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
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
RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction
May 18, 2025Zewei Ye, Weifeng Lu, Minghao Ye +4Robot Failure Recovery Robustness of VLA Models
School of AI, Shanghai Jiao Tong University · Harbin Institute of Technology, Shenzhen
Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability
Date pendingYudong Gao, Linghan Chen, Wenhan Wu +5Adversarial Attacks Adversarial Attacks on VLA Models
The Hong Kong University of Science and Technology · Adelaide University · Wuhan University +3

May 18, 2026, cs.CVJ/K move · Enter open · S save

StableVLA: Towards Robust Vision-Language-Action Models without Extra Data

Yiyang Fu, Chubin Zhang, Shukai Gong, Yufan Deng+6

Peking University · Tsinghua University · Astribot+2

Image Corruption Robustness Vision-Language-Action Models Robustness of VLA Models

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It is infeasible to encompass all possible disturbances within the training dataset. This raises a critical question regarding the robustness of Vision-Language-Action (VLA) models when encountering unseen real-world visual disturbances, particularly under imperfect visual conditions. In this work, we conduct a systematic study based on recent state-of-the-art VLA models and reveal a significant performance drop when visual disturbances absent from the training data are introduced. To mitigate this issue, we propose a lightweight adapter module grounded in information theory, termed the Information Bottleneck Adapter (IB-Adapter), which selectively filters potential noise from visual inputs. Without requiring any extra data or augmentation strategies, IB-Adapter consistently improves over the baseline by an average of 30%, while adding fewer than 10M parameters, demonstrating notable efficiency and effectiveness. Furthermore, even with a 14x smaller backbone (0.5B parameters) and no pre-training on the Open X-Embodiment dataset, our model StableVLA achieves robustness competitive with 7B-scale state-of-the-art VLAs. With negligible parameter overhead (<10M), our approach maintains accuracy on long-horizon tasks and surpasses OpenPi under both synthetic and physical visual corruptions.

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