A Unifying Perspective for Safety of Stochastic Systems: From Barrier Functions to Finite Abstractions

TL;DR

This paper unifies stochastic barrier functions and finite abstraction methods under a stochastic dynamic programming framework, clarifying their theoretical properties and trade-offs in safety guarantees for stochastic systems.

Contribution

It provides a unifying theoretical perspective that connects existing safety methods, establishing their correctness, convergence, and optimality properties.

Findings

Both methods are approximations of stochastic dynamic programming.

Abstraction-based approaches can achieve asymptotic optimality.

SBFs are computationally less demanding but less optimal.

Abstract

Providing safety guarantees for stochastic dynamical systems is a central problem in various fields, including control theory, machine learning, and robotics. Existing methods either employ Stochastic Barrier Functions (SBFs) or rely on numerical approaches based on finite abstractions. SBFs, analogous to Lyapunov functions, are used to establish (probabilistic) set invariance, whereas abstraction-based approaches approximate the stochastic system with a finite model to compute safety probability bounds. This paper presents a unifying perspective on these seemingly different approaches. Specifically, we show that both methods can be interpreted as approximations of a stochastic dynamic programming problem. This perspective allows us to formally establish the correctness of both techniques, characterize their convergence and optimality properties, and analyze their respective assumptions, advantages, and limitations. Our analysis reveals that, unlike SBFs-based methods, abstraction-based approaches can provide asymptotically optimal safety certificates, albeit at the cost of increased computational effort.