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About

Yue Gu is a researcher whose work bridges the frontiers of modular robotics, fluidic propulsion, and human-swarm interaction. Gu’s primary contributions lie in the innovative design of modular fluidic propulsion (MFP) robots, which function as reconfigurable fluid networks that move by routing fluid through themselves—a concept that promises scalable, highly adaptable locomotion for aquatic environments. Their foundational 2016 paper on modular hydraulic propulsion (15 citations) established this paradigm, while the 2020 work on MFP robots (21 citations) refined the concept, demonstrating how effective propulsion can be combined with a large reconfiguration space. Demonstrating versatility, Gu also explored theoretical machine learning in a 2017 paper (11 citations) that generalized Generative Adversarial Networks through a Turing perspective. More recently, Gu has focused on human-swarm interaction, investigating how operators can maintain situational awareness during complex swarm missions, with a 2023 study (4 citations) and a 2025 user study (2 citations) on predictive formal modelling at runtime. This trajectory from hardware innovation to human-robot teaming showcases a researcher deeply engaged with both the physical and cognitive challenges of next-generation robotic systems.