发表机构
RWTH Aachen University; University of Technology Nuremberg; Technical University of Munich（亚琛工业大学; 纽伦堡工业大学; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述贝叶斯优化在控制器调参和机器人学习中的十年进展，提供教程式设置指南、方法分类，并针对控制应用缺乏标准基准的问题，提出轻量级基准套件及评估指标。
AI 中文摘要
在过去十年中，贝叶斯优化（BO）已成为自动控制器调参和机器人学习的一个强大且适应性强的框架。本文全面概述了BO的最新进展，旨在帮助研究人员和实践者理解最新进展、实际应用和未来研究方向。我们首先从实践者的角度出发，通过一个代表性的控制器调参示例，说明如何有效设置BO。我们将BO置于更广泛的学习范式背景中，从深度强化学习到数据驱动控制，并强调BO最具优势的场景。接下来，我们讨论为应对复杂问题和特定应用而开发的各种BO方法。本文对BO当前格局提供了统一视角，强调其与控制系统和机器人的相关性，并通过识别关键研究挑战和有前景的推进方向来突出未来前景。这包括解决BO格局中的一个显著空白：缺乏专门针对控制相关应用的标准化基准问题。为了促进未来研究并确保严格评估，我们启动了一项构建面向控制工程和机器人的轻量级基准套件的努力。我们还提出了指标和最佳实践，以促进新BO算法与既有最先进方法之间的直接比较。
英文摘要
In the past decade, Bayesian optimization (BO) has emerged as a powerful and adaptable framework for automatic controller tuning and robot learning. This article offers a comprehensive overview of the state-of-the-art in BO, designed to support both researchers and practitioners in understanding recent advancements, practical applications, and future research directions. We begin by adopting a practitioner's perspective, illustrating how to effectively set up BO through a representative controller tuning example. We position BO within the broader context of learning paradigms, ranging from deep reinforcement learning to data-driven control, and highlight scenarios where BO is most advantageous. Next, we discuss the diverse range of BO methods that have been developed to tackle complex problems and specific applications. This article provides a unified perspective on the current landscape of BO, emphasizing its relevance to control systems and robotics, and it highlights future prospects by identifying key research challenges and promising avenues for advancing BO in the field. This includes addressing a significant gap in the BO landscape: the lack of standardized benchmark problems specifically for control-related applications. To foster future research and ensure rigorous evaluation, we start an effort towards a lightweight benchmark suite for control engineering and robotics. We also present metrics and best practices to facilitate direct comparisons between new BO algorithms and established state-of-the-art methods.
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