TMT: Runtime Backdoor Detection for Vision-Language-Action Policies on Unseen Tasks

Summary

TMT is a runtime backdoor detector for vision-language-action (VLA) policies, built on token manifold and latent transition modeling and trained on benign rollouts. The authors also explore policy purification through self-distillation, using a frozen copy of the backdoored policy as a teacher. In a post-hoc comparison with ten baselines, TMT achieves state-of-the-art detection on unseen tasks across three VLA backdoor attacks.

Topics

• Robotics and embodied AI

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