We present a model-free reinforcement learning algorithm to synthesize control policies that
maximize the probability of satisfying high-level control objectives given as Linear Temporal …

In this paper, we develop a method to automatically generate a control policy for a
dynamical system modeled as a Markov Decision Process (MDP). The control specification …

In this paper we present a method for automatic planning of optimal paths for a group of
robots that satisfy a common high-level mission specification. The motion of each robot is …

We present a method for designing a robust control policy for an uncertain system subject to
temporal logic specifications. The system is modeled as a finite Markov Decision Process …

Formal methods are increasingly being used for control and verification of dynamic systems
against complex specifications. In general, these methods rely on a relatively simple system …

This article considers control synthesis of an autonomous agent with linear temporal logic
(LTL) specifications subject to environment and motion uncertainties. Specifically, the …

With the rapid development of software and distributed computing, Cyber-Physical Systems
(CPS) are widely adopted in many application areas, eg, smart grid, autonomous …

We consider the problem of automatic control strategy synthesis, for discrete models of
robotic systems, to fulfill a task that requires reaching a goal state while obeying a given set …

This paper studies motion planning of a mobile robot under uncertainty. The control
objective is to synthesize a finite-memory control policy, such that a high-level task specified …

This paper studies the problem of control strategy synthesis for dynamical systems with
differential constraints to fulfill a given reachability goal while satisfying a set of safety rules …