Yuze Wu

Huzhou University, Zhejiang University, Wuhan University

Papers

Total Citations

H-Index

About

Yuze Wu is a rising researcher in robotics, specializing in autonomous navigation, active reconstruction, and legged locomotion. Their work bridges critical gaps in robotic autonomy, particularly for complex, multimodal systems. Wu’s most impactful contribution is the development of an autonomous and adaptive navigation framework for terrestrial-aerial bimodal vehicles (2022, 66 citations), which enables seamless transitions between ground and air travel—a breakthrough for search-and-rescue and environmental monitoring. This work addresses the longstanding challenge of integrating high mobility with endurance in hybrid robots. More recently, Wu introduced GS-Planner (2024, 22 citations), a Gaussian-splatting-based planning framework for active high-fidelity 3D reconstruction, overcoming the limitations of traditional scene representations to produce realistic, full-coverage models. This innovation promises to revolutionize autonomous data capture in construction and digital twin creation. Additionally, Wu’s exploration of hierarchical reinforcement learning for multiple-gait quadrupedal locomotion (2023) demonstrates a commitment to advancing agile, adaptive robot movement. With a growing citation record and a focus on practical, high-impact solutions, Wu is establishing themselves as a key innovator in autonomous systems and robotic perception.