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Welcome to the Center for Autonomy

The Center for Autonomy brings together several research groups that address fundamental challenges in developing autonomous systems through contributions in controls, machine learning, game theory, information theory, and formal methods. Its primary objective is to create a unified front in attracting the best researchers to UT Austin and empowering them to solve the pressing problems toward developing autonomous systems that can make a net positive impact.

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News

An example gameplay. In this example, the nonchameleons (blue players) correctly identify the chameleon (red player), but the chameleon wins in the second chance.

Study Shows Strategic Weakness in AI During Deception Games

Nov. 5, 2025

Researchers Mustafa Karabag and Ufuk Topcu from 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.

Photo of Summer 2025 Interns and Mentors

Robotics, AI and Programming: The Center for Autonomy Summer Research Internship Programs

Oct. 20, 2025

The Center for Autonomy at the Oden Institute for Computational Engineering and Sciences hosted four outreach programs for undergraduates over the summer. Over the course of the three eight-week programs, AEOP, Realtime Adaption REU and NASA's ULI, interns contributed to both software and hardware engineering tasks, supporting ground and legged platforms such as Clearpath Jackals, a Husky, and Unitree Go2 quadrupeds. Under the guidance of research mentor Dr. Christian Ellis and Ph.D. students, participants break down complex research objectives into achievable tasks, ultimately developing software to work seamlessly with real robotic systems.

The Future of Programming in the Age of Large Language Models, CCC Whitepaper Release

May 15, 2025

The CCC just released a new joint whitepaper with CRA-Industry, "The Future of Programming in the Age of Large Language Models." A collaborative effort of leading experts in academia and industry, this paper delves into the profound impact of large language models (LLMs) on the landscape of software development and computing education.

Research Projects

Figure explaining Basis to Basis operators

Basis-to-Basis Operator Learning Using Function Encoders

Operator learning is a new frontier in computational learning-based approaches for autonomy that enables us to work directly with function-to-function mappings.

AI generated photo of city with skyscrapers to demonstrate new Chishik.AI research

Chishiki.ai: An integrated community of Computational Infrastructure professionals (CIPs) across AI and CEE sectors

The Chishiki project adopts four strategies to build a sustainable, diverse, and integrated community of CI professionals for AI in CEE by (1) fostering collaboration between CIPs and domain experts through initiatives such as research summits, graduate and undergraduate fellowships, joint research initiatives, and industrial partnerships; (2) offering personalized and scalable learning environments powered by AI; (3) developing innovative AI-enabled CI architectures for reproducible and efficient workflows; and (4) creating a diverse, sustainable CIP community through engagement with historically underrepresented institutions through recruitment and research initiatives.

Autonomous Aerial Cargo Operations at Scale

The project addresses the challenge of "safe and efficient growth in global operations, with the expected outcome of an algorithmic foundation that will help realize increasingly autonomous and collaborative air traffic management for all classes of airspace and vehicles and support scalable and efficient operations that rapidly adapt to meet changing demands and to respond to system disruptions.