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About

Charlie Street is an emerging robotics researcher whose work sits at the intersection of multi-robot systems, formal methods, and decision-making under uncertainty. His research addresses one of the central challenges in deploying robot teams in real-world environments: ensuring robust, coordinated behaviour when execution is unpredictable. Street is perhaps best known for his pioneering work on congestion-aware planning, recognising that spatial congestion — robots impeding one another in shared environments — is a critical and often overlooked source of performance degradation. His 2021 paper on congestion-aware policy synthesis (24 citations) established formal frameworks for maintaining system performance under such conditions, building on earlier probabilistic planning models developed in 2020. Beyond congestion, Street has broadened his contributions to encompass proactive task allocation under spatiotemporal uncertainty, heterogeneous multi-robot formation coordination for object transportation, and verifiable deliberation systems for autonomous robots. His 2023 review of formal modelling formalisms serves as a valuable resource for researchers entering the field. Affiliated with the Goal-Oriented Long-Lived Systems Lab, Street's growing body of work — accumulating over 60 citations across eight publications — reflects a rigorous, theoretically grounded approach to making robot teams reliably deployable in complex, dynamic environments.