This paper focuses on the need for a rigorous theory of layered control architectures (LCAs)
for complex engineered and natural systems, such as power systems, communication …

Enabling the capability of assessing risk and making risk-aware decisions is essential to
applying reinforcement learning to safety-critical robots like drones. In this paper, we …

For highly automated driving above SAE level 3, behavior generation algorithms must
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …

We propose a novel reinforcement learning methodology where the system performance is
evaluated by a Markov coherent dynamic risk measure with the use of linear value function …

Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover
operations on deformable terrains but suffers from inevitable prediction errors. Especially for …

In this article, we propose a novel safety specification tool, called the distributionally robust
risk map (DR-risk map), for a mobile robot operating in a learning-enabled environment …

We present a framework to interpret signal temporal logic (STL) formulas over discrete-time
stochastic processes in terms of the induced risk. Each realization of a stochastic process …

Safely deploying robots in uncertain and dynamic environments requires a systematic
accounting of various risks, both within and across layers in an autonomy stack from …

Decision-making under uncertainty is a crucial ability for autonomous systems. In its most
general form, this problem can be formulated as a partially observable Markov decision …

We study the problem of incorporating risk while making combinatorial decisions under
uncertainty. We formulate a discrete submodular maximization problem for selecting a set …