发表机构
San Diego State University; University of California San Diego（圣地亚哥州立大学; 加州大学圣地亚哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究有损编码器何时允许安全证书迁移到物理系统，发现取决于被丢弃安全动力学的可检测性，并给出线性情形下的迁移条件及两个显式裕度。
AI 中文摘要
潜在安全滤波器在状态的学习低维表示上认证安全性，从而能够施加难以进行解析描述的约束。由于编码器是有损的，滤波器可能报告安全而物理状态不安全，且没有可检测的模型误差。现有的迁移条件将丢弃的安全信息的效应隐含化。我们探究有损编码器何时允许一个可迁移到物理系统的安全证书，并表明答案由被丢弃的安全相关动力学的可检测性所支配。我们构造了一个系统，其潜在模型是精确的，且其潜在信号总是报告安全，而物理状态变得任意不安全。对于该系统，不存在证书，且编码器下游的任何监视器都无法检测到该故障。当被丢弃的动力学收缩时，一个潜在屏障以两个显式裕度认证真实安全性，一个针对潜在模型误差，另一个针对编码器无法区分的状态间安全性的变化。在线性情形下，在有界性和非退化条件下，当安全相关子空间可检测时，每个校准的屏障都以有限裕度迁移，否则没有一个能迁移。在学习得到的车杆编码器上，模型误差并不指示对于哪些表示估计的界是非空的，而第二个裕度则能指示。
英文摘要
Latent safety filters certify safety on a learned low-dimensional representation of the state, enabling constraints that resist analytic description. Because the encoder is lossy, a filter can report safe while the physical state is unsafe, with no detectable model error. Existing transfer conditions leave the effect of discarded safety information implicit. We ask when a lossy encoder admits a safety certificate that transfers to the physical system, and show the answer is governed by the detectability of the discarded safety-relevant dynamics. We construct a system whose latent model is exact and whose latent signals always report safe, while the physical state becomes arbitrarily unsafe. For this system no certificate exists and no monitor downstream of the encoder can detect the failure. When the discarded dynamics contract, a latent barrier certifies true safety up to two explicit margins, one for the latent-model error and one for the variation of safety across states the encoder cannot distinguish. In the linear case and under boundedness and non-degeneracy conditions, every calibrated barrier transfers with a finite margin when the safety-relevant subspace is detectable, and none does otherwise. On learned cartpole encoders, the model error does not indicate for which representations the estimated bound is non-vacuous, while the second margin does.
