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). Prior to joining Cambridge, Amanda was a postdoctoral researcher at the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania, USA, where she worked with Prof. Vijay Kumar. She completed her PhD at EPFL, Switzerland, with Prof. Alcherio Martinoli.
The lab's research focuses on multi-agent and multi-robot systems. Our 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. This research brings in methods from machine learning, planning, and control, and has numerous applications, including automated transport and logistics, environmental monitoring, surveillance, and search.
Our research is supported by the ERC, Arm, Amazon, the Distributed and Collaborative Intelligent Systems and Technology Collaborative Research Alliance (CRA), Nokia Bell Labs, and UKRI's Engineering and Physical Sciences Research Council.
University of Cambridge
William Gates Building
15 JJ Thomson Avenue
Cambridge CB3 0FD, UK
asp45 at cam.ac.uk
Interviewed by Robohub Podcast - listen here. (Published on 03/2021)
The website for IEEE MRS is live! Submit your best papers here. (Published on 02/2021)
Recipient of this year’s ERC Starting Grants. I am tremendously excited to work on my project, ‘gAIa’, which aims to co-optimize robotic mobility and the environment. See the story. (Published on 09/2020)
Our research on cooperative driving is featured in BBC4 program The Hidden Wilds of the Motorway (start at 1:19:40). (Published on 07/2020)
Recipient of this year's Amazon Research Awards, for our research on learning how to communicate to coordinate (in multi-robot systems). (Published on 06/2020)
Feature article on our research and my personal story thus far. (Published on 06/2020)
Delighted that our paper “Multi-Vehicle Mixed Reality Reinforcement Learning for Autonomous Multi-Lane Driving” has been selected for inclusion in the 'Best Papers' volume from AAMAS workshops, to be published by Springer. (Published on 06/2020)
Speaking at the Workshop on Foundational Problems in Multi-robot Coordination under Uncertainty and Adversarial Attacks at ICRA, June 4, 2020. (Published on 05/2020)
Speaking at the Workshop on Heterogeneous Multi-Robot Task Allocation and Coordination at RSS, July 12, 2020. (Published on 04/2020)
Research Assistant to work on ML-based sensor fusion.
Project Coordinator to support the lab in day-to-day operations.
PhD applicants who wish to work in my lab are encouraged to apply here.
Q. Li, W. Lin, Z. Liu, A. Prorok, Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning, IEEE Robotics and Automation Letters (R-AL), 2021. PDF
R. Kortvelesy, A. Prorok, ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture. ICRA, 2021. PDF
M. Malencia, V. Kumar, G. J. Pappas, A. Prorok, Fair Robust Assignment using Redundancy. IEEE Robotics and Automation Letters (R-AL), 2021. Preprint.
J. Blumenkamp, A. Prorok, The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning. Conference on Robot Learning (CoRL), 2020. PDF.
J. Gielis, A. Prorok, Improving 802.11p for Delivery of Safety Critical Navigation Information in Robot-to-Robot Communication Networks. IEEE Communications Magazine, vol. 59, no. 1, pp. 16-21, January 2021. PDF.
B. Wang, Z. Liu, Q. Li, A. Prorok, Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning. IEEE Robotics and Automation Letters (R-AL), vol 5:4, pp. 6932-6939, 2020. Preprint.
Q. Li, F. Gama, A. Ribeiro, A. Prorok, Graph Neural Networks for Decentralized Multi-Robot Path Planning. PDF. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020