Home | Center for Autonomy

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.

Upcoming Events

Practical Challenges for Compositional RL

Event Status

Scheduled

Friday March 24, 2023, 1:30 pm - 2:30 pm

POB 6.304

Dr. Jared Culbertson is a research mathematician with the U.S. Air Force Research Laboratory's Autonomous Capabilities Team (ACT3), a research group focused on the development and deployment of flexible AI solutions across a diverse set of air and space mission areas. Jared's research primarily deals with fundamental aspects of representational structures, recently involving compositional approaches for hybrid dynamical systems and now focused on behavior acquisition, diversity, and composition in reinforcement learning problems.

Data-Driven Control under Input and Measurement Noise

Event Status

Scheduled

Monday April 10, 2023, 11:00 am - 12:00 pm

POB 6.304

Jared Miller is a 5th year PhD Student at the Robust Systems Lab at Northeastern University, advised by Mario Sznaier. He received his B.S. and M.S. degrees in Electrical Engineering from Northeastern University in 2018. He is a recipient of the 2020 Chateaubriand Fellowship from the Office for Science Technology of the Embassy of France in the United States. He was given an Outstanding Student Paper award at the IEEE Conference on Decision and Control in 2021 and in 2022. His current research topics include safety verification and data-driven control. His interests include large-scale convex optimization, nonlinear systems, semi-algebraic geometry, and measure theory.

News

NASA Awards Advance 3D Printing, Quantum Tech for Climate Research

March 16, 2023

Texas Engineers are leading a multi-university research team, funded by NASA’s Space Technology Mission Directorate, that will build technology and tools to improve measurement of important climate factors by observing atoms in outer space. They will focus on the concept of quantum sensing, which involves observing how atoms react to small changes in their environment, and using that to infer the time-variations in the gravity field of the Earth. This will enable scientists to improve how accurately several important climate processes can be measured, such as the sea level rise, the rate of ice melt, the changes in land water resources and ocean heat storage changes.

Autonomous Aerial Cargo Operations at Scale Roundtable

Feb. 6, 2023

The "Autonomous Aerial Cargo Operations at Scale" program held a roundtable at Marshall University in Huntington, West Virginia. This roundtable aimed to validate their CONOPS that characterized six relevant operational scenarios. The purpose was to define research questions that the team would address and to identify potential datasets or information that could be used to answer these questions. The team sought feedback and discussions with local Appalachia stakeholders to determine if the proposed scenarios were realistic, feasible, and pertinent and to enable autonomous operations in both rural Appalachia and dense metropolitan areas. Participants engaged with experts from Purdue University, MIT, MIT Lincoln Laboratory, Morgan State University, and Cavan Solutions.

Research Projects

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.

Learning Mixed Strategies in Trajectory Games

In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another’s behavior. Trajectory games capture these complex effects by design.

A recent paper by members of the DCIST alliance develops a perception-action-communication loop framework using Vision-based Graph Aggregation and Inference (VGAI). This multi-agent decentralized learning-to-control framework maps raw visual observations to agent actions, aided by local communication among neighboring agents.

Learning Decentralized Controllers with Graph Neural Networks

Outreach Events