About ARIA Systems Group

We are a group of robot enthusiasts in the Departments of Aerospace Engineering Sciences and Computer Science at the University of Colorado Boulder set on developing Assured, Reliable, and Interactive Autonomous (ARIA) systems. We envision a world where autonomous systems operate safely and effectively alongside humans and form trusting partnerships to improve the well-being of individuals and societies. This vision drives our research in developing theoretical foundations and computational frameworks that enable reliable and intelligent autonomy. We view this as an art, a creative process that requires deep technical understandings of the fields that contribute to robotics.

The main theme of our work is safety and soundness, and the emphasis is on safe autonomy through correct-by-construction algorithmic approaches. Our research builds on knowledge developed in control theory, formal methods, statistical reasoning, machine learning & AI and seeks to address real-world challenges in robotics and safety-critical systems.

Group News

17 Jul 2025

Anne Theurkauf successfully defended her PhD dissertation. Congrats Anne!

15 Jul 2025

Our work on "Bayesian Diagnosability and Active Fault Identification" has been accepted for presentation at IEEE Conference on Decision and Control (CDC) 2025.

15 Jul 2025

Our work on "On Polynomial Stochastic Barrier Functions: Bernstein Versus Sum-of-Squares" has been accepted for presentation at IEEE Conference on Decision and Control (CDC) 2025. Read the preprint here

15 Jul 2025

Our work on "Piecewise Control Barrier Functions for Safe Control of Stochastic Systems" has been accepted for presentation at IEEE Conference on Decision and Control (CDC) 2025.

10 Jul 2025

Robert Reed presented his paper on ‘data-driven shielding’ at the American Control Conference (ACC) 2025. Read the preprint here!

20 Jun 2025

Morteza Lahijanian gave an invited talk in the RSS Workshop Fast motion planning and control in the era of parallelism. Watch it here

06 May 2025

Our work on "Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs" has been accepted for presentation in the conference on Uncertainty in Artificial Intelligence (UAI). Read the preprint here!