Xiufang Shi

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About

Xiufang Shi is a rising researcher in embodied AI and multi-robot systems, with a focus on enabling intelligent navigation in complex, real-world environments. Her work spans three critical frontiers: semantic reasoning for object navigation, adaptive formation control for robot swarms, and robust sensor fusion for state estimation. In her highly cited work "ChatNav," Shi pioneered the use of large language models for zero-shot semantic reasoning in object goal navigation, allowing robots to understand environmental relationships without prior training on 3D datasets—a breakthrough that has already garnered 16 citations since 2024. Her "DEFORM" system introduces adaptive formation reconfiguration for multi-robot teams navigating confined, obstacle-rich spaces, addressing a long-standing challenge in swarm robotics. Additionally, her "AF-RLIO" framework achieves robust odometry by adaptively fusing radar, LiDAR, and inertial data, maintaining precise pose estimation even in smoke, tunnels, and adverse weather where single-sensor systems fail. With over 20 citations across her recent publications, Shi’s work is shaping the next generation of autonomous navigation, demonstrating that robots can perceive, reason, and adapt in environments that were previously inaccessible.