Reachability analysis is a formal method to guarantee safety of dynamical systems under the
influence of uncertainties. A substantial bottleneck of all reachability algorithms is the …

In this work, we analyze an efficient sampling-based algorithm for general-purpose
reachability analysis, which remains a notoriously challenging problem with applications …

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 …

We study the convex hulls of reachable sets of nonlinear systems with bounded
disturbances. Reachable sets play a critical role in control, but remain notoriously …

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 …

Partially autonomous driving systems may require the human driver to take control at any
moment, yet by their design, they often cause difficulty with attention management. In this …

We consider estimating a compact set from finite data by approximating the support function
of that set via sublinear regression. Support functions uniquely characterize a compact set …

We consider the problem of forward reachability analysis of a black-box nonlinear system,
using only the data from the system. We propose a method that computes an ellipsoidal set …

We present a method to overapproximate forward stochastic reach sets of discrete-time,
stochastic nonlinear systems with interval geometry. This is made possible by extending the …