Professor of Collective Intelligence and Robotics
Amanda Prorok
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 IEEE 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.
Publications
- Concrete multi-agent path planning enabling kinodynamically aggressive maneuvers npj Robotics 2026
- When Is Diversity Rewarded in Cooperative Multi-Agent Learning? ICLR 2026 2026
- Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding ICLR 2026 2026
- Remotely Detectable Robot Policy Watermarking ICLR 2026 2026
- No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes NeurIPS 2025 2025
- Graph Attention-Guided Search for Dense Multi-Agent Pathfinding AAAI 2026 2026
- Extending robot minds through collective learning Science Robotics 2025
- ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination CoRL 2025 2025
- D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations IROS 2025 2025
- DVM-SLAM: Decentralized Visual Monocular Simultaneous Localization and Mapping for Multi-Agent Systems ICRA 2025 2025
- Language-Conditioned Offline RL for Multi-Robot Navigation ICRA 2025 2025
- Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling ICLR 2025 2025
- Co-Optimizing Reconfigurable Environments and Policies for Decentralized Multi-Agent Navigation IEEE Transactions on Robotics 2025
- BenchMARL: Benchmarking Multi-Agent Reinforcement Learning JMLR 2024
- Provably Safe Online Multi-Agent Navigation in Unknown Environments CoRL 2024 2024
- CoViS-Net: A Cooperative Visual Spatial Foundation Model for Multi-Robot Applications CoRL 2024 2024
- Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning ICML 2024 2024
- The Cambridge RoboMaster: An Agile Multi-Robot Research Platform DARS 2024 2024
- Generalised f-Mean Aggregation for Graph Neural Networks NeurIPS 2023 2023
- Reinforcement Learning with Fast and Forgetful Memory NeurIPS 2023 2023
- Heterogeneous multi-robot reinforcement learning AAMAS 2023 2023
- POPGym: Benchmarking Partially Observable Reinforcement Learning ICLR 2023 2023