Zong-Ze Wu

Papers

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About

Zong-Ze Wu is a robotics researcher whose work focuses on the intersection of autonomous navigation, human-robot interaction, and visual servoing for mobile manipulators. His primary research areas include social robot navigation and time-delay compensation in visual control systems. Wu’s most notable contribution is the development of **Composite Reinforcement Learning for Social Robot Navigation**, a framework that moves beyond traditional path-planning metrics to enable service robots to navigate in human-shared environments with social awareness. This work, which has garnered **24 citations**, addresses the critical need for robots to consider human comfort and social norms during movement, rather than simply minimizing travel distance. In parallel, Wu has explored **visual servoing with time-delay compensation** for humanoid mobile manipulators, tackling the practical challenges of latency in image processing and data transmission that plague commercial robotic platforms. His research is particularly valuable for real-world applications where robots must operate alongside people, such as in service or assistive contexts. By combining reinforcement learning with social constraints and addressing hardware limitations, Wu’s work contributes to making autonomous robots more adaptable, socially competent, and reliable in dynamic environments.