Statistical Reachability Analysis of Stochastic Cyber-Physical Systems under Distribution Shift

• URL: http://arxiv.org/abs/2407.11609v1
• Date: Tue, 16 Jul 2024 11:18:41 GMT
• Title: Statistical Reachability Analysis of Stochastic Cyber-Physical Systems under Distribution Shift
• Authors: Navid Hashemi, Lars Lindemann, Jyotirmoy V. Deshmukh,
• Abstract summary: Reachability analysis is a popular method to give safety guarantees for cyber-physical systems. In this paper, we investigate the problem of performing reachability analysis for an SCPS that does not have a symbolic description of the dynamics. We propose a statistical reachability analysis technique that guarantees that any reachable state during deployment lies in this set with probability not smaller than this threshold.
• Score: 0.061446808540639365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reachability analysis is a popular method to give safety guarantees for stochastic cyber-physical systems (SCPSs) that takes in a symbolic description of the system dynamics and uses set-propagation methods to compute an overapproximation of the set of reachable states over a bounded time horizon. In this paper, we investigate the problem of performing reachability analysis for an SCPS that does not have a symbolic description of the dynamics, but instead is described using a digital twin model that can be simulated to generate system trajectories. An important challenge is that the simulator implicitly models a probability distribution over the set of trajectories of the SCPS; however, it is typical to have a sim2real gap, i.e., the actual distribution of the trajectories in a deployment setting may be shifted from the distribution assumed by the simulator. We thus propose a statistical reachability analysis technique that, given a user-provided threshold $1-\epsilon$, provides a set that guarantees that any reachable state during deployment lies in this set with probability not smaller than this threshold. Our method is based on three main steps: (1) learning a deterministic surrogate model from sampled trajectories, (2) conducting reachability analysis over the surrogate model, and (3) employing {\em robust conformal inference} using an additional set of sampled trajectories to quantify the surrogate model's distribution shift with respect to the deployed SCPS. To counter conservatism in reachable sets, we propose a novel method to train surrogate models that minimizes a quantile loss term (instead of the usual mean squared loss), and a new method that provides tighter guarantees using conformal inference using a normalized surrogate error. We demonstrate the effectiveness of our technique on various case studies.

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