  Bayesian optimization (BO) has proven to be a powerful tool for automatically
tuning control parameters without requiring knowledge of the underlying system
dynamics. Safe BO methods, in addition, guarantee safety during the
optimization process, assuming that the underlying objective function does not
change. However, in real-world scenarios, time-variations frequently occur, for
example, due to wear in the system or changes in operation. Utilizing standard
safe BO strategies that do not address time-variations can result in failure as
previous safe decisions may become unsafe over time, which we demonstrate
herein. To address this, we introduce a new algorithm, Event-Triggered SafeOpt
(ETSO), which adapts to changes online solely relying on the observed costs. At
its core, ETSO uses an event trigger to detect significant deviations between
observations and the current surrogate of the objective function. When such
change is detected, the algorithm reverts to a safe backup controller, and
exploration is restarted. In this way, safety is recovered and maintained
across changes. We evaluate ETSO on quadcopter controller tuning, both in
simulation and hardware experiments. ETSO outperforms state-of-the-art safe BO,
achieving superior control performance over time while maintaining safety.
