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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
What do robots need to know?: World representations for autonomous systems
Wednesday, September 23, 2026
4:00pm Central Time
In Person Event - 1232 CSL Studio
Speaker: Victor Romero Cano (Cardiff University)
For a robot to act autonomously in the world, it must first understand it. But what does that understanding look like, and how should it be structured? This talk explores the question of world representation from the perspective of autonomous systems operating in complex, real-world environments, asking not just what robots can perceive, but what they need to know to act effectively.
We trace a research thread that begins with dynamic scene understanding through moving object detection and simultaneous multi-object tracking and classification, where probabilistic methods enable robots to reason jointly about the identity and motion of multiple agents. Alongside this, we address perception in autonomous driving, from stereo- and LiDAR-based motion detection to dense outdoor perception, semantic occupancy grids, and driver behaviour modelling, illustrating how rich world representations underpin safe operation in unstructured, populated environments. Moving from perception to action, we discuss view planning strategies that actively direct a robot's sensors toward task-relevant information, and learning-informed motion planning approaches that embed prior experience into the planning process to handle constrained workspaces and goal uncertainty. Each of these contributions highlights how the demands of the task shape the representation a robot must maintain.
Throughout, a consistent theme emerges: effective autonomy requires layered world representations that integrate geometry, semantics, dynamics, and structure. We reflect on the design principles that make such representations tractable and useful, and consider how they connect to the broader ambitions of embodied AI, where perception, memory, and action are tightly coupled in agents that must think, learn, and act in the physical world.