通过核函数选择提升安全贝叶斯优化，高效解决多控制器高维机器人控制问题。
Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization
- 基于核函数选择改进加性高斯过程，提升高维控制优化效率。
- 在无人机PID控制器优化中，相比现有方法显著减少迭代次数。
- 适用于需要安全约束的复杂机器人系统控制优化，如飞行器与机械臂。
控制系统优化一直是机器人领域的核心挑战。尽管近年来出现了基于学习的方法（如SafeOpt）用于优化单个反馈控制器，但将其扩展到具有多个控制器的高维复杂系统仍存在难题。本文提出一种新型学习型控制优化方法，通过核函数选择增强基于加性高斯过程的安全贝叶斯优化算法，以更高效地处理高维问题。以无人机的PID控制器优化为例，在专为评估安全控制技术设计的Safe Control Gym基准上进行测试。结果表明，该方法在高维控制优化中提供了更高效且更优的解决方案，相较现有技术有显著提升。
原文摘要 · Abstract (English)
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.
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