News

Postdoctoral Researcher, Dr. Neel Bhatt from The Center for Autonomy was recently featured on CBS Austin News for the Tech This Out segment, highlighting the Center's work on creating AI systems that can be reliably verified. Researchers are developing methods to ensure autonomous systems behave predictably, embedding constraints and testing frameworks so decisions can be explained and certified. This approach supports safer, more accountable deployment of robotics and autonomous vehicles in real-world environments.

Autonomous systems deployed in real-world environments must operate safely despite uncertainty, adaptation, and changing conditions. Researchers in the Center for Autonomy at UT Austin are developing dynamic certification frameworks that continuously evaluate system behavior instead of relying solely on static pre-deployment testing. The goal is to provide more robust safety assurance for autonomous vehicles and other robotic systems as they are increasingly deployed outside controlled settings.

Ensuring the safety of autonomous systems requires certification methods that can keep pace with systems that learn, update, and operate in changing environments. Researchers at The University of Texas at Austin and collaborating institutions are developing new frameworks that move beyond traditional static certification approaches. The multi-university effort integrates expertise in controls, formal methods, machine learning, human factors, robotics, and systems engineering to study scalable methods for assuring safety in real-world autonomous systems.

John‑Paul Clarke, a professor of aerospace engineering at The University of Texas at Austin, was elected to the National Academy of Engineering. Clarke is recognized for pioneering aircraft arrival procedures that allow planes to glide during descent, reducing fuel use, emissions, and noise, and now used at major airports worldwide. His work in aircraft trajectory optimization and air traffic operations has helped improve the efficiency and environmental performance of modern aviation systems.

An expanded partnership connects local high school students directly with researchers to engage in the design and implementation of autonomous systems. The AutoDriveLab Internship, a collaboration between The University of Texas at Austin’s Center for Autonomy and the Del Valle Independent School District (DVISD), recently completed its inaugural cohort. Throughout the fall semester (Figure 1), interns worked alongside graduate students and postdoctoral researchers from the Center for Autonomy to design, assemble, and program autonomous vehicles.

The magazine article “A Road Map for Responsible Robotics,” published in IEEE Robotics and Automation Magazine, presents the outcomes of the Dagstuhl Seminar “Roadmap for Responsible Robotics,” held in September 2023 at the Leibniz Center for Informatics in Germany. The seminar brought together researchers from robotics, computer science, social and cognitive sciences, and philosophy to chart a path toward strengthening responsibility in robotic systems.

Thinh Doan, an assistant professor in the Department of Aerospace Engineering and Engineering Mechanics (ASE/EM) and member of the Center for Autonomy, has been honored with the 2025 Antonio Ruberti Young Researcher Prize. The award, established in 2005, is given annually by the Institute of Electrical and Electronics Engineers (IEEE) Control Systems Society (CSS) to “recognize outstanding achievement in research in systems and control by a young researcher and to honor the memory of Dr. Antonio Ruberti.”

The Computing Community Consortium (CCC), together with the IEEE Computer Society, held a two-day workshop titled Computing on the Fly Workshop on December 1–2, 2025 at The Darcy Hotel in Washington, D.C. The event gathered experts from robotics, AI, distributed systems, and other areas to explore the future of drone- and edge-computing through 2035.

Professor Ufuk Topcu presented at The Exchange: AI + Autonomy, hosted by the Special Competitive Studies Project (SCSP) on September 25, 2025, in Austin, TX. The event convened leaders from government, industry, and academia to discuss how artificial intelligence and autonomy are shaping national security.

Mustafa Karabag and Prof. Ufuk Topcu from The Center for Autonomy at UT Austin found that large language models excel at inferring hidden information but struggle to withhold it, often revealing too much in social deduction games like The Chameleon. Their findings expose a key weakness in AI’s strategic reasoning, particularly in adversarial or high-stakes scenarios where discretion is critical.