We develop a sampling-based motion planning algorithm that combines long-term temporal
logic goals with short-term reactive requirements. The mission specification has two parts:(1) …

In this article, we present a hierarchical multirate control architecture for nonlinear
autonomous systems operating in partially observable environments. Control objectives are …

The deployment of autonomous systems in uncertain and dynamic environments has raised
fundamental questions. Addressing these is pivotal to build fully autonomous systems and …

Autonomous systems are often required to operate in partially observable environments.
They must reliably execute a specified objective even with incomplete information about the …

We propose a novel planning technique for satisfying tasks specified in temporal logic in
partially revealed environments. We define high-level actions derived from the 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 …

The wide availability of data coupled with the computational advances in artificial
intelligence and machine learning promise to enable many future technologies such as …

The synthesis of controllers guaranteeing linear temporal logic specifications on partially
observable Markov decision processes (POMDP) via their belief models causes …

This paper introduces the Logic-based Value Iteration Network (LVIN) framework, which
combines imitation learning and logical automata to enable agents to learn complex …

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 …