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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.

Photo of Center for Autonomy group members

Upcoming Events

Youssef Hashash, Ph.D., P.E., F. ASCE, NAE Professor

Webinar on: Transformative Potential of Machine Learning and AI in Geotechnical Engineering

Event Status

Scheduled

Wednesday May 14, 2025, 12:00 pm - 1:00 pm

In this presentation we explore the past, present and future potential of AI in Geotechnical Engineering. The presentation will provide a brief history and context of AI and its uses in Civil Engineering in general and Geotechnical Engineering in particular. The presentation will then track developments over the past three decades including the use of Artificial Neural Networks for developing correlations in lieu of conventional regression analysis, as well as the use of ANN to represent material constitutive behavior. The presentation will also explore the role of AI in inverse analysis and discovering material behavior. Lastly the presentation will delve into the world of Large Language Models and Generative AI application in Geotechnical Engineering with several illustrative examples.

News

Cyrus Neary Wins 2025 Oden Institute Outstanding Dissertation Award

April 29, 2025

The Oden Institute for Computational Engineering and Sciences has named Cyrus Neary the recipient of its 2025 Outstanding Dissertation Award. Neary, a former GRA with the Autonomous Systems Group, graduated with his Ph.D. in Computational Science, Engineering, and Mathematics in August 2024 and will return to Austin this May for the commencement ceremony. He is currently a postdoctoral researcher at Mila - The Quebec AI Institute, and he will join the University of British Columbia as an assistant professor in the fall of 2025, where he looks forward to continuing to advance his work at the intersection of machine learning, autonomy, and robotics.

Professor Ufuk Topcu and Christian Ellis, a joint postdoctoral researcher with the Army Research Laboratory and the UT Center for Autonomy

UT Leads Defense Research in Robotics

Feb. 11, 2025

The University of Texas at Austin, in partnership with the U.S. Department of Defense, is advancing robotics and AI systems to enhance military safety and efficiency. Professor Ufuk Topcu and Christian Ellis, a joint postdoctoral researcher with the Army Research Laboratory and the UT Center for Autonomy, have developed practical solutions for real-world robotic platforms and applications, including a reinforcement learning approach for autonomous ground vehicles operating in unstructured terrain.

Robots belonging to Texas Robotics stroll through Seedway as part of the UT Year of AI Robot Parade on Nov 15. 2024

Texas Robotics Hosts Robot Parade as Grand Finale of UT’s Year of AI

Nov. 20, 2024

As the grand finale to UT Austin's Year of AI, members from the Center for Autonomy participated in a Robot Parade organized by Texas Robotics on Friday, November 15, 2024. Group members Will Ward and Christian Ellis drove our lab's Clearpath robots and Unitree Go 2 robotic dog alongside UT Mariachi and a parade of students, faculty, and researchers.

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.