ICG Lab Series Talk: Amanda Prorok, University of Cambridge

Abstract: How are we to orchestrate large teams of agents? How do we distill global goals into local robot policies? Machine learning has revolutionized the way in which we address these questions by enabling us to automatically synthesize decentralized agent policies from global objectives. In this presentation, I first describe how we leverage data-driven approaches to learn interaction strategies that lead to coordinated and cooperative 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 will present some of our results on cooperative perception, coordinated path planning, and close-proximity quadrotor flight. To conclude, I discuss the impact of policy heterogeneity on agent alignment and sim-to-real transfer.

About the Speaker: Amanda Prorok is Professor of Collective Intelligence and Robotics in the Department of Computer Science and Technology, at Cambridge University, and a Fellow of Pembroke College. Her lab's research focuses on multi-agent and multi-robot systems. Their mission is to find new ways of coordinating artificially intelligent agents (e.g., robots, vehicles, machines) to achieve common goals in shared physical and virtual spaces. Together with her lab, Prorok pioneered methods for differentiable communication between learning agents. Their research brings in methods from machine learning, planning, and control, and has numerous applications, including automated transport and logistics, environmental monitoring, surveillance, and search.

Prior to joining Cambridge, Amanda was a postdoctoral researcher at the General Robotics, Automation, Sensing and Perception (GRASP, opens an external URL in a new window) Laboratory at the University of Pennsylvania, USA. She completed her PhD at EPFL, opens an external URL in a new window, Switzerland. She has been honored by numerous research awards, including an ERC Starting Grant, an Amazon Research Award, the EPSRC New Investigator Award, the Isaac Newton Trust Early Career Award, and several Best Paper awards. Her PhD thesis was awarded the Asea Brown Boveri (ABB) prize for the best thesis at EPFL in Computer Science. She serves as Associate Editor for IEEE Robotics and Automation Letters (R-AL) and Associate Editor for Autonomous Robots (AURO).

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