Recent literature has proposed approaches that learn control policies with high performance
while maintaining safety guarantees. Synthesizing Hamilton-Jacobi (HJ) reachable sets has …

We present SOCKS, a data-driven stochastic optimal control toolbox based in kernel
methods. SOCKS is a collection of data-driven algorithms that compute approximate …

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 …

In this paper, we compute finite sample bounds for data-driven approximations of the
solution to stochastic reachability problems. Our approach uses a nonparametric technique …

This report presents the results of a friendly competition for formal verification and policy
synthesis of stochastic models. It also introduces new benchmarks within this category, and …

Cyber-physical systems tightly integrate computational resources with physical processes
through sensing and actuating, widely penetrating various safety-critical domains, such as …

We present algorithms for performing data-driven stochastic reachability as an addition to
SReachTools, an open-source stochastic reachability toolbox. Our method leverages a class …

Recent literature has proposed approaches that learn control policies with high performance
while maintaining safety guarantees. Synthesizing Hamilton-Jacobi (HJ) reachable sets has …

Autonomous systems are increasingly being deployed in complex environments subject to
real-world uncertainty. For such systems, it may be exceptionally difficult or even impossible …