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

We propose a new task-specification language for Markov decision processes that is
designed to be an improvement over reward functions by being environment independent …

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 …

Designing reliable decision strategies for autonomous urban driving is challenging.
Reinforcement learning (RL) has been used to automatically derive suitable behavior in …

We describe a computational framework for automatic deployment of a robot with sensor
and actuator noise from a temporal logic specification over a set of properties that are …

We address the problem of verifying Probabilistic Computation Tree Logic (PCTL) properties
of Markov Decision Processes (MDPs) whose state transition probabilities are only known to …

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

Unmanned aerial systems provide many applications with the ability to perform flying tasks
autonomously, and hence have received significant research and commercial attention in …

Optimal control policy synthesis for probabilistic systems from high-level specifications is
increasingly often studied. One major question that is commonly faced, however, is what to …

Autonomous spacecraft control via Shielded Deep Reinforcement Learning (SDRL) has
become a rapidly growing research area. However, the construction of shields and the …