Distributed Multi-robot Control: Physics, Geometry and Learning

Abstract — Multi-robot systems appear as a promising solution for tackling complex tasks that are beyond the capabilities of a single robot. Their inherent parallelism, robustness to individual failures, and ability to run in large-scale environments make them particularly appealing for applications such as search and rescue, environmental monitoring, herding and agriculture, or warehouse automation. However, the coordination and control of multiple robots operating in a distributed infrastructure poses significant challenges. Specifically, this talk elaborates on three key aspects of distributed multi-robot systems: effectiveness of coordination in highly nonlinear, volatile environments; fast and accurate reconstruction of collective information; and scalability of the control policies in the number of robots. To address them, the work presented in the talk exploits three main tools: physical properties of networked systems, geometrical control techniques, and distributed optimization and machine learning methods.

Bio — Eduardo is a postdoctoral researcher at the ProrokLab, at the University of Cambridge. He earned a PhD in the Robotics, Computer Vision and Artificial Intelligence group of the Universidad de Zaragoza, supervised by Eduardo Montijano and Carlos Sagüés. He has been a visiting scholar at the Existential Robotics Laboratory at UCSD in 2022 and 2024. His research interests span topics related to multi-robot decision-making, networked system, control and learning (and, occasionally, power electronics). Eduardo is a Fulbright Scholar and a DAAD AInet Fellow.