Exploiting Vulnerabilities: Universal Adversarial Attacks on Vision-Language-Action Models in Robotics

Summary

Researchers propose a Universal Adversarial Object, a sphere with an optimized surface texture, that degrades the task success of Vision-Language-Action (VLA) models when placed in a robot's field of view. Their multi-level attack framework disrupts trajectory planning, task execution and action control, and was validated in simulated and real-world robotic settings. The object reduces average task success rates by 31.2% to 39.9% for two VLA models, Pi0 and RDT, with success rates dropping to near zero in complex scenarios.

Topics

• Adversarial machine learning
• Robotics and embodied AI

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