arXiv:2610.23746v1 Announce Type: new Abstract: Robust control uses the error between a known model and the true system as a robustness margin. However, this error is usually unknown: conservative estimates restrict feasibility and performance, while underestimates can result in violations of the control property. We estimate this robustness margin from online data using e-values, a statistical tool for hypothesis testing, in the setting of discrete-time control barrier functions (CBFs). Here, we measure the robustness margin as the difference between the observed CBF and the model-based CBF prediction. We then design a robust CBF controller in three steps by: (1) selecting a set of candidate margins, (2) obtaining validity certificates for these candidates via sequential tests based on e-values, and (3) updating the CBF controller with the smallest certified candidate. The deployed tests accumulate evidence against the hypothesis that an exceedance risk, the probability of the robustness margin exceeding the candidate margin, remains at or above a pre-specified failure probability $\alpha$. Our tests are anytime valid and thus enable the use of certified candidates in the CBF controller, guaranteeing that the probability that the exceedance risk of a certified margin exceeds $\alpha$ is at most a user-chosen confidence $\delta$. We validate our approach on navigation problems across different environments with visualizations in NVIDIA Isaac Sim.