TAPDreamer: Transferable Adversarial Patches for World Action Models

Summary

TAPDreamer is an attack on world action models that builds a fixed local adversarial patch using only a public encoder, with no queries to the target policy. The patch, covering about 6.5% of the input, transfers across tasks and action architectures. In closed-loop tests it cut FastWAM's success rate from 97.7% to 0.0% on 40 LIBERO tasks and from 90.86% to 0.0% on 50 RoboTwin tasks, and it also lowered success on two DreamWAM configurations and on Motus.

Topics

• Adversarial machine learning

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