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Robotic algorithms typically depend on various parameters, the choice ofwhich significantly affects the robot's performance. While an initial guess forthe parameters may be obtained from dynamic models of the robot, parameters areusually tuned manually on the real system to achieve the best performance.Optimization algorithms, such as Bayesian optimization, have been used toautomate this process. However, these methods may evaluate unsafe parametersduring the optimization process that lead to safety-critical system failures.Recently, a safe Bayesian optimization algorithm, called SafeOpt, has beendeveloped, which guarantees that the performance of the system never fallsbelow a critical value; that is, safety is defined based on the performancefunction. However, coupling performance and safety is often not desirable inrobotics. For example, high-gain controllers might achieve low average trackingerror (performance), but can overshoot and violate input constraints. In thispaper, we present a generalized algorithm that allows for multiple safetyconstraints separate from the objective. Given an initial set of safeparameters, the algorithm maximizes performance but only evaluates parametersthat satisfy safety for all constraints with high probability. To this end, itcarefully explores the parameter space by exploiting regularity assumptions interms of a Gaussian process prior. Moreover, we show how context variables canbe used to safely transfer knowledge to new situations and tasks. We provide atheoretical analysis and demonstrate that the proposed algorithm enables fast,automatic, and safe optimization of tuning parameters in experiments on aquadrotor vehicle.

Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics