カーネル選択による安全ベイズ最適化を用いたロボット制御の最適化
Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization
加法ガウス過程ベースのSafe Bayesian Optimizationにカーネル選択を導入し、ドローンのPID制御最適化など高次元制御問題を効率的に解く手法を提案した論文。
著者: Lihao Zheng, Hongxuan Wang, Xiaocong Li, Jun Ma, Prahlad Vadakkepat
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open problem. In this paper, we propose a novel learning-based control optimization method, which enhances the additive Gaussian process-based Safe Bayesian Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a representative example and test the method on Safe Control Gym, a benchmark designed for evaluating safe control techniques. We show that the proposed method provides a more efficient and optimal solution for high-dimensional control optimization problems, demonstrating significant improvements over existing techniques.
