Event-Triggered Structured Active Exploration with Gaussian Process Safety Filters

For robotic systems operating under uncertain environmental conditions, accurate system models are often unavailable. To ensure their safety despite this uncertainty, the integration of Gaussian process (GP) models into safety filters based on control barrier function (CBF) has been shown to be promising. Since these GP-CBF safety filters become infeasible when the model uncertainty is too large, they are typically updated online, but existing strategies for data generation insufficiently cover the control space, introduce excessive conservatism, and temporarily compromise safety. We address this problem by proposing an event-triggered structured exploration approach for safe and sample-efficient learning of CBF-based safety filters using GPs. Before infeasibility issues can occur, our approach activates a safe exploratory control sequence, which is constructed via orthogonal perturbations of a safe control direction. Under standard regularity assumptions and sufficient CBF robustness, we prove high-probability recursive feasibility and safety, as well as finite-trigger and thereby sample complexity guarantees. Experiments on two safe robot control benchmarks show improved safety rates with lower conservatism and lower update frequency than state-of-the-art baselines. Manipulator simulations demonstrate that the GP model underlying the safety filter can be trained solely using distance-derived labels for certain system classes.