Robot control for tasks such as moving around obstacles or grasping objects has advanced
significantly in the last few decades. However, controlling robots to perform complex tasks is …

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 …

This letter investigates the motion planning of autonomous dynamical systems modeled by
Markov decision processes (MDP) with unknown transition probabilities over continuous …

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

Time series anomaly detection has become a crucial and challenging task driven by the
rapid increase of streaming data with the arrival of the Internet of Things. Existing methods …

In this paper, we propose a model-free reinforcement learning method to synthesize control
policies for mobile robots modeled as Markov Decision Process (MDP) with unknown …

This paper studies optimal motion planning subject to motion and environment uncertainties.
By modeling the system as a probabilistic labeled Markov decision process (PL-MDP), the …

This paper presents a model-free reinforcement learning (RL) algorithm to synthesize a
control policy that maximizes the satisfaction probability of complex tasks, which are …

This paper studies satisfaction of temporal properties on unknown stochastic processes that
have continuous state spaces. We show how reinforcement learning (RL) can be applied for …

Safety is crucial for robotic missions within an uncertain environment. Common safety
requirements such as collision avoidance are only state-dependent, which can be restrictive …