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About

Minyue Fu is a robotics and artificial intelligence researcher whose work spans autonomous navigation, multi-agent systems, and human-robot interaction. His most impactful contribution lies in 2D LiDAR-based SLAM and path planning for indoor rescue robotics, a paper with 79 citations that addresses the critical challenge of enabling mobile robots to autonomously map and navigate cluttered, unknown environments during emergency response operations. Fu has also made significant contributions to multi-agent coverage search, surveying strategies for teams of robots to systematically explore unknown environments with obstacles—work that bridges computational geometry, artificial intelligence, and practical deployment. His research extends into formation control for distributed robot systems under directed and switching topologies, where he developed linear approaches for robots relying only on local sensing information. More recently, Fu has explored interactive AI systems for educational applications and in-context imitation learning through the In-Context Robot Transformer (ICRT), which enables robots to perform novel tasks by autoregressive prediction from demonstration examples. With publications spanning from foundational control theory to cutting-edge transformer-based robot learning, Fu’s work demonstrates a trajectory from practical rescue robotics toward generalizable, data-driven robot intelligence.