Are Vision-Language-Action Models Robust to One-Step Observation Perturbations?
Understanding the safety risks of vision-language-action (VLA) models is essential for their deployment in the physical world. Existing safety research has mainly considered persistent perturbations that are applied continuously to observations throughout an episode. However, momentary observation corruption, in which observations are severely perturbed only briefly within an episode, remains an underexplored safety threat. To address this gap, this work investigates robustness to one-step perturbations applied at a single time step per episode. Our experiments reveal that these perturbations substantially degrade VLA performance and that their impact depends on the action chunk execution...
| Authors | Shojiro Yamabe, Jun Sakuma |
|---|---|
| Primary category | cs.AI |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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