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About

Guyue Zhou is a pioneering robotics researcher whose work spans 3D scene understanding, autonomous navigation, assistive robotics, and human-robot interaction. Zhou's research tackles fundamental challenges in enabling robots to perceive, interpret, and interact with complex real-world environments. Among Zhou's most influential contributions is the PQ-Transformer, a unified neural architecture that simultaneously parses 3D objects and room layouts from point clouds, elegantly solving the inefficiency of separate task-specific networks. This work reflects a broader commitment to elegant, computationally efficient 3D perception, further demonstrated through LODE's implicit scene completion from sparse LiDAR data and LATITUDE's city-scale neural radiance field localization system—each addressing critical autonomous driving and navigation challenges. Particularly notable is Zhou's dedication to socially impactful robotics. Research exploring guiding robots for blind and low vision individuals—examining quadruped guides, autonomy levels, and participatory design—demonstrates a rare integration of technical innovation with genuine human-centered thinking. The affective touch computing system "Touch-and-Heal" further highlights this human-robot interaction focus. With over 220 cumulative citations across recent publications alone, and contributions ranging from visual sensing platforms to curriculum-based loco-manipulation control, Zhou represents a versatile researcher whose work meaningfully advances both the intelligence and accessibility of modern robotic systems.