Home

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.

Photo of Center for Autonomy group members

Upcoming Events

Center for Autonomy and AEOP Summer 2026 Internship

Event Status

Scheduled

Monday June 1, 2026 - Monday August 10, 2026, All day

The Army Educational Outreach Program (AEOP) summer internship, “Verifiable Integration of Multi-Modal Pre-Trained Generative Models in Sequential Decision-Making for Autonomous Systems,” provides summer support for two qualified undergraduate students to work with faculty, mentors, and research staff from the Center of Autonomy during the summer months. Click for more info on this paid internship opportunity!

News

Reflections from CCC’s “Computing on the Fly” Workshop

Dec. 5, 2025

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.

Prof. Ufuk Topcu Speaks at The Exchange: AI + Autonomy

Nov. 18, 2025

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.

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. 12, 2025

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.

Cooperative Bargaining Games Without Utilities: Mediated Solutions from Direction Oracles

Figure: Formations achieved by different bargaining solutions

Cooperative Bargaining Games Without Utilities: Mediated Solutions from Direction Oracles

We introduced a bargaining method that uses agents’ preferred directions rather than utility values.

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.