Safe Controller Optimization for Quadrotors with Gaussian Processes
This addresses the safety-critical tuning of controllers for dynamic systems like quadrotors, representing an incremental improvement by applying an existing safe optimization method to a new domain.
The paper tackled the problem of automatic controller parameter tuning for quadrotors, which traditionally requires manual adjustments and risks safety-critical failures. It applied the SafeOpt algorithm to guarantee safety during optimization, achieving fast and automatic tuning without human intervention.
One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance. To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used. However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen. In this paper, we overcome this problem by applying, for the first time, a recently developed safe optimization algorithm, SafeOpt, to the problem of automatic controller parameter tuning. Given an initial, low-performance controller, SafeOpt automatically optimizes the parameters of a control law while guaranteeing safety. It models the underlying performance measure as a Gaussian process and only explores new controller parameters whose performance lies above a safe performance threshold with high probability. Experimental results on a quadrotor vehicle indicate that the proposed method enables fast, automatic, and safe optimization of controller parameters without human intervention.