Enhancing Visual Task-Redirection Attacks on Vision-Language-Action Models via Closed-Loop Optimization

Summary

Researchers show that visual perturbations can redirect vision-language-action robot models away from the user's intended task. Prior attacks used fixed target supervision, which stops matching the states the attacked robot actually reaches. The proposed TRACO framework rebuilds supervision from the states reached under attack and focuses updates where task progress stalls, improving task-redirection success across VLA models and simulation environments.

Topics

• Robotics and embodied AI

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