Stanford Seminar - Learning to control large teams of robots

May 9, 2025 Eduardo Montijana, Univ. de Zaragoza Controlling large teams of robots is a crucial challenge in robotics, due to the need for solutions that balance efficiency, scalability, and robustness. This talk will delve into recent advancements in learning-based control for multi-robot systems, with a focus on scalable coordination and decentralized decision-making. I will present the latest results we have achieved to efficiently learn distributed control strategies for large teams of robots, leveraging physics-informed machine learning and generative AI techniques. We will see how physics-informed learning can be used to provide the learned controllers three key properties: interpretability, modularity, and scalability. Similarly, we will demonstrate how generative AI can be used to ease interaction with non-expert users to describe desired large-scale swarm configurations, producing smooth trajectories and accounting for potential collisions through a reactive navigation algorithm. About the speaker: https://sites.google.com/unizar.es/eduardo-montijano More about the course can be found here: https://stanfordasl.github.io/robotics_seminar/ View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD ► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore View our Robotics and Autonomous Systems Graduate Certificate: https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-certificate