Title:

Synthesizing Multi-Robot Policies: From Cooperative Perception to Human-Led Fleet Control

Abstract:

How are we to orchestrate large teams of robots? How do we distill global goals into local robot policies? And how do we seamlessly integrate human-led fleet control? Machine learning has revolutionized the way in which we address these questions by enabling us to automatically synthesize agent policies from high-level objectives. In this presentation, I first describe how we leverage data-driven approaches to learn interaction strategies that lead to coordinated and cooperative robot behaviors. I will introduce our work on Graph Neural Networks, and show how we use such architectures to learn multi-agent policies through differentiable communications channels. I present experimental results with mobile robots engaged in cooperative perception, formation control, and human-led path-finding; I also show how the methods scale to very large-scale systems, and how they are capable of modeling complex physical interactions in close-proximity flight with multiple quadrotors.

Speaker’s Bio:

Amanda Prorok is Professor of Collective Intelligence and Robotics in the Department of Computer Science and Technology at the University of Cambridge, and a Fellow of Pembroke College. In her work, she pioneered differentiable communications methods for multi-agent systems, with applications to multi-robot perception and control. Amanda has given invited keynotes at TEDx and ICRA, and has been honored by numerous research awards, including a prestigious ERC Starting Grant. Amanda is an IEEE Senior Member, serves as Associate Editor for Autonomous Robots (AURO) and was the Chair of the 2021 IEEE International Symposium on Multi-Robot and Multi-Agent Systems. Her PhD thesis was awarded the Asea Brown Boveri (ABB) prize for the best thesis at EPFL in Computer Science.