Zongkai Wu

Westlake University, Harbin Engineering University

Papers

Total Citations

H-Index

About

Zongkai Wu is a robotics and artificial intelligence researcher whose work bridges autonomous navigation, human-robot interaction, and deep learning. His research focuses primarily on path planning for mobile robots and the emerging field of vision-and-language navigation (VLN), where agents must interpret natural language instructions alongside real-time visual input to move intelligently through real-world environments. Wu's most influential contribution, "A Global Path Planning Algorithm for Robots Using Reinforcement Learning" (2019, 44 citations), addressed critical shortcomings in traditional algorithms like BFS and RRT by developing a smoother, more efficient navigation solution powered by reinforcement learning. This work has become a notable reference point in autonomous mobile robotics research. Building on this foundation, Wu expanded into the complex challenge of VLN, publishing two significant papers in 2021. His meta-learning approach to visual perception generalization (27 citations) demonstrated creative problem-solving in enabling agents to adapt across diverse environments — a persistent challenge in the field. His earlier work on manipulator remote control systems (2014) reveals a career rooted in practical robotics hardware, lending depth and real-world grounding to his increasingly sophisticated AI-driven research. Wu's trajectory reflects a researcher steadily advancing toward fully autonomous, language-guided robotic systems.