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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

Teaching robots to think on the fly

April 5, 2023

Commercial airplanes can be controlled by autopilot. But what happens if a wing gets damaged or an engine malfunctions? Is it possible to design a software system with a feedback loop — a system that quickly tests how controls operate on the damaged vessel and makes adjustments on the fly to give it the best chance of landing safely?

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

Del Valle Outreach Visit

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