🤖 AI Summary
This work addresses the vulnerability of Vision-Language-Action (VLA) robotic systems in wireless sensor networks to physical adversarial attacks, with a particular focus on motion-guided visual attention hijacking as a critical threat. The authors propose VLAGuard, the first framework to systematically evaluate and effectively mitigate this vulnerability. It introduces Visual-motor Attention-guided Semantic Attack (VASA), a printable patch-based stress test, and Attention-Protected Fine-Tuning (APFT), a defense method that stabilizes spatiotemporal attention and enforces geometric consistency without incurring any inference overhead. Experimental results demonstrate that VLAGuard reduces the failure rate of OpenVLA from 100.0% to 25.9% in the LIBERO simulation benchmark and improves task success under severe attacks from 23.0% to 67.4% across 2,000 real-world trials.
📝 Abstract
Deploying Vision-Language-Action (VLA) robots as mobile edge nodes within wireless sensor networks (WSNs) requires robust protection against physical adversarial threats. We present VLAGuard, a framework to assess and mitigate a critical vulnerability: policy-critical action-to-vision attention hijacking. We first introduce a stress-test module, Visuomotor Attention-guided Semantic Attack (VASA), using printable patches to severely distract the robot's action-conditioned cross-attention. To counter this, we propose Attention-Protective Fine-Tuning (APFT), a defense that stabilizes spatiotemporal attention and enforces geometric consistency with zero inference overhead. Evaluations across simulated and physical WSN-assisted smart environments demonstrate significant robustness gains. APFT reduces the OpenVLA failure rate from 100.0% to 25.9% in LIBERO simulations. Furthermore, across 2,000 real-world trials, APFT improves the average success rate from 23.0% to 67.4% under severe patch attacks. This highlights that protecting attention pathways is important for improving the robustness of VLA-driven edge nodes in sensor networks.