让数据驱动控制同时保证安全与性能，不牺牲任何训练数据。
Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guarantees
- 用统一框架融合学习与验证，所有数据用于联合设计与认证。
- 在最优控制等任务中表现优于主流方法，兼具高安全性和强性能。
- 适合需要严格安全保障的复杂系统，如自动驾驶与工业控制。
数据驱动方法在日益复杂的系统与控制问题中愈发重要。但在安全关键场景中部署这些方法，需提供严格保障，这推动了统计学习与控制交叉领域的大量研究。然而，许多现有方法需预留数据用于测试或校准，或限制学习算法选择，导致性能下降。本文提出面向系统与控制的 Pick-to-Learn（P2L）框架，使任意数据驱动控制方法均可获得当前最先进的安全与性能保障。P2L 允许全部可用数据用于联合合成与认证，无需专门保留数据进行校准或验证。通过全面展示 P2L 在系统与控制中的应用，本文证明其在最优控制、可达性分析、安全合成与鲁棒控制等核心问题上的有效性。在多个任务中，P2L 生成的设计与证书均优于常用方法，展现出在多样化实际场景中的广泛应用潜力。
原文摘要 · Abstract (English)
Data-driven methods have become paramount in modern systems and control problems characterized by growing levels of complexity. In safety-critical environments, deploying these methods requires rigorous guarantees, a need that has motivated much recent work at the interface of statistical learning and control. However, many existing approaches achieve this goal at the cost of sacrificing valuable data for testing and calibration, or by constraining the choice of learning algorithm, thus leading to suboptimal performances. In this paper, we describe Pick-to-Learn (P2L) for Systems and Control, a framework that allows any data-driven control method to be equipped with state-of-the-art safety and performance guarantees. P2L enables the use of all available data to jointly synthesize and certify the design, eliminating the need to set aside data for calibration or validation purposes. In presenting a comprehensive version of P2L for systems and control, this paper demonstrates its effectiveness across a range of core problems, including optimal control, reachability analysis, safe synthesis, and robust control. In many of these applications, P2L delivers designs and certificates that outperform commonly employed methods, and shows strong potential for broad applicability in diverse practical settings.
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