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Center for Autonomy
Enabling high-impact research in autonomous system design
About
The Center for Autonomy (CfA) is Illinois's interdisciplinary hub for autonomy and robotics research and education in the Grainger College of Engineering at University of Illinois Urbana-Champaign. The CfA houses the Master of Engineering in Autonomy and Robotics, manages a number of robotics labs, fosters community, and helps incubate high-impact research endeavors.
From self-driving cars to intelligent robotic assistants to remote surgical systems, autonomous technology will revolutionize the way we live, work, and play. In order to enable this revolution, however, advancements in foundational research and workforce development must first take place to provide assured and certified-safe performance.
Application areas include:
UPCOMING & RECENT EVENTS
Structure-Preserving Learning for Autonomy in Complex Physical Environments
Friday, November 13, 2026
2:00pm Central Time
In Person Event - CSL Studio Room 1232
Speaker: Ani Hsieh (University of Pennsylvania )
Autonomous robots are increasingly expected to operate in environments where the surrounding physics cannot be ignored, whether ground vehicles traversing unstructured terrain or aerial and marine vehicles navigating geophysical flows. In these settings, the robot and its environment form a tightly coupled dynamical system, and the performance of the robot depends on how well this coupling is understood. These interactions are often highly nonlinear, time-varying, and difficult to model from first principles alone, which makes reliable autonomy fundamentally challenging. Advances in data-driven methods have opened new pathways to modeling these complex environments and interactions. However, these approaches have limited interpretability and generalizability, require large amounts of training data that is often costly to obtain, and fail to preserve the very mathematical properties that make real-time planning and control tractable. This raises fundamental questions about how learning should be done for autonomy: what should be learned, what should be known a priori, and what structure must be preserved. In this talk, I will show that embedding physical structure and preserving mathematical structure are key to answering these questions. Using tight formation flight of quadrotors as an example, I will present our knowledge-based neural ordinary differential equations (KNODE) framework for learning residual dynamics and show how it can be combined with adaptive control to yield sample-efficient and robust model predictive controllers. I will then show how learning residuals that preserve differential flatness leads to controllers that are both computationally lightweight and high-performing. Finally, I will discuss how these ideas extend to modeling the environment itself, from learned finite element models that exactly preserve conservation laws for multi-robot source localization in complex flows to learned Lagrangian models that enable stable long-horizon forecasting. I will conclude by showing how preserving structure can lead to new planning strategies that combine the guarantees of model-based approaches with the generalizability of learned ones.