王晓飞 题为 “Partially Observable Adversarial Patch Attacks on Vision-Language-Action Models in Robotics” 的论文已被《IEEE Robotics and Automation Letters》接受发表。该论文摘要如下：

Vision–language–action (VLA) models are gaining attention in robotics, yet their robustness to adversarial attacks remains largely unexplored. Existing work shows that adversarial patches can mislead VLA-based robots but assumes full access to the entire execution trajectory, an unrealistic requirement in practice. We address this limitation by formulating a partially observable threat model, where the adversary can exploit only a short prefix of the trajectory to generate a fixed patch applied to all subsequent frames. Under this setting, we propose a two-phase framework. First, we localize the patch using the model’s attention maps to identify visually critical regions that correspond to the full instruction. Then, we optimize the patch to disrupt the semantic grounding of target objects and increase the curvature of action trajectories, thereby compounding failures in both perception and control. Extensive experiments in simulation and real-world robotic environments show that our method sustains strong adversarial effects under partial observability, inducing long-horizon disruptions and significantly reducing task success rates. Codes will be made public upon paper acceptance.