用最优控制理论指导采样，让安全过滤器更快更准地学会避险。
Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle
- 基于庞特里亚金极小值原理，识别临界避险轨迹以指导数据采集。
- 在赛车共享控制实验中，学习效率提升，失败率降低，3毫秒内完成推理。
- 适合需要实时安全保障的高维自主系统，如自动驾驶车辆。
安全滤波器为自主系统施加安全约束提供了一种实用方法。尽管基于学习的工具可扩展至高维系统，但其性能依赖于包含可能导致约束违反状态的有信息量数据，而在复杂高维系统中高效采样此类数据颇具挑战。本文利用庞特里亚金极小值原理刻画几乎避免安全违规的轨迹，这些边界轨迹用于引导学习哈密顿-雅可比可达性的数据收集，使学习集中于安全临界状态附近，从而提升效率。所学的控制屏障值函数直接用于安全滤波。仿真与共享控制汽车竞速应用的实验验证表明，基于PMP的采样显著提升学习效率，实现更快收敛、更低失败率及更优安全集重构，计算耗时约3毫秒。
原文摘要 · Abstract (English)
Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning-based tools scale to high-dimensional systems, their performance depends on informative data that includes states likely to lead to constraint violation, which can be difficult to efficiently sample in complex, high-dimensional systems. In this work, we characterize trajectories that barely avoid safety violations using the Pontryagin Maximum Principle. These boundary trajectories are used to guide data collection for learned Hamilton-Jacobi Reachability, concentrating learning efforts near safety-critical states to improve efficiency. The learned Control Barrier Value Function is then used directly for safety filtering. Simulations and experimental validation on a shared-control automotive racing application demonstrate PMP sampling improves learning efficiency, yielding faster convergence, reduced failure rates, and improved safe set reconstruction, with wall times around 3ms.
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