We introduce a framework for the control of discrete-time switched stochastic systems with
uncertain distributions. In particular, we consider stochastic dynamics with additive noise …

This article addresses the problem of temporal-logic-based planning for bipedal robots in
uncertain environments. We first propose an Interval Markov Decision Process abstraction of …

Providing safety guarantees for stochastic dynamical systems has become a central problem
in many fields, including control theory, machine learning, and robotics. Existing methods …

Algorithmic verification of realistic systems to satisfy safety and other temporal requirements
has suffered from poor scalability of the employed formal approaches. To design systems …

This paper proposes a new framework to compute finite-horizon safety guarantees for
discrete-time piece-wise affine systems with stochastic noise of unknown distributions. The …

Distributionally robust optimization (DRO) incorporates robustness against uncertainty in the
specification of probabilistic models. This paper focuses on mitigating the curse of …

In this paper, we introduce a data-driven framework for synthesis of provably-correct
controllers for general nonlinear switched systems under complex specifications. The focus …

In this paper, we present a novel data-driven approach to quantify safety for non-linear,
discrete-time stochastic systems with unknown noise distribution. We define safety as the …

In this paper, we present a novel framework to synthesize robust strategies for discrete-time
nonlinear systems with random disturbances that are unknown, against temporal logic …