Large-scale robot fleets are increasingly deployed in warehouses, factories, transportation systems, and emerging robotics applications. Coordinating hundreds or thousands of robots in shared, cluttered spaces creates fundamental challenges in maintaining safety, preventing deadlocks, and minimizing congestion. In this talk, I will present our recent work on scalable imitation learning methods for coordinating 10k robots, automatic environment optimization techniques for alleviating traffic congestion, and asynchronous execution frameworks that guarantee safe, robust, and deadlock-free multi-robot operations.

Jiaoyang Li is an Assistant Professor in the Robotics Institute at Carnegie Mellon University. Her research lies at the intersection of artificial intelligence, robotics, and optimization, with a particular focus on scalable multi-robot planning and coordination. She received her Ph.D. in Computer Science from the University of Southern California in 2022 and her B.Eng. in Automation from Tsinghua University in 2017. She is a recipient of the NSF CAREER Award, best dissertation awards from ICAPS, AAMAS, and USC, and multiple best paper awards from ICRA, ICAPS, and MRS.