Providing non-trivial certificates of safety for non-linear stochastic systems is an important
open problem. One promising solution to address this problem is the use of barrier functions …

Leveraging autonomous systems in safety-critical scenarios requires verifying their
behaviors in the presence of uncertainties and black-box components that influence the …

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 …

Neural networks (NNs) are emerging as powerful tools to represent the dynamics of control
systems with complicated physics or black-box components. Due to complexity of NNs …

Verifying the performance of safety-critical, stochastic systems with complex noise
distributions is difficult. We introduce a general procedure for the finite abstraction of …

We study the problem of refining satisfiability bounds for partially-known stochastic systems
against planning specifications defined using syntactically co-safe Linear Temporal Logic …

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 …

We present a novel framework for formal control of uncertain discrete-time switched
stochastic systems against probabilistic reach-avoid specifications. In particular, we consider …

Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models,
where the transition probabilities belong to intervals. Recently, there has been a surge of …

Deep Kernel Learning (DKL) combines the representational power of neural networks with
the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising tool …