Noise-Space Safety Corrector for Vision-Language-Action Models

Vision-Language-Action (VLA) models have demonstrated strong generalization capabilities across diverse robotic manipulation tasks. However, physical deployment of these models requires satisfying safety constraints while retaining task performance. Existing approaches enforce safety by correcting actions during sampling or projecting them after generation. Although these approaches produce safe actions, they either ignore chunk level dependencies within action sequences or push actions away from the learned behavior of the pretrained policy, thereby degrading task performance. To address these limitations, we propose Noise Safe Corrector (NSC), a test-time safety filter that corrects the noise of a pretrained VLA policy. As the transformation from the noise to the action chunk is deterministic, we can optimize the noise to enforce safety constraints while taking chunk level dependencies into account and preserving pretrained knowledge encoded in the policy. To enforce safety constraints, we use a control barrier function (CBF) formulated in noise space and iteratively solve a quadratic program (QP), which has a closed-form solution. We further prove that NSC obtains a safe action chunk within a finite number of iterations under mild assumptions. Experiments across three simulation environments and real-world robotic tasks show that NSC outperforms baselines in terms of task success rate while satisfying safety constraints.