Are Vision-Language-Action Models Robust to One-Step Observation Perturbations?
TL;DR
This work investigates robustness to one-step perturbations applied at a single time step per episode and proposes CARE, which dynamically selects the execution length based on consistency with the previously predicted action chunk, which improves robustness with low computational overhead while preserving clean performance by selecting shorter execution lengths only under perturbations.
Abstract
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 length. Based on them, we propose CARE, which dynamically selects the execution length based on consistency with the previously predicted action chunk. CARE improves robustness with low computational overhead while preserving clean performance by selecting shorter execution lengths only under perturbations.
