Zengwen Wu

Papers

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About

Zengwen Wu is a robotics researcher focused on integrating computer vision with human-robot interaction to address real-world challenges in healthcare, agriculture, and eldercare. His work centers on developing intelligent robotic systems that can perceive and respond to their environments, with particular emphasis on assistive technologies for aging populations and automated solutions for food handling. Wu's notable contributions include a vision-based fruit packaging robot (2021, 3 citations), which leverages deep learning to automate agricultural sorting and packing—a critical innovation for reducing human contact during the COVID-19 pandemic. He also designed a vision-based chess detection system for a robotic companion (2021, 2 citations), enabling elderly individuals living alone to engage in interactive gameplay and receive emotional support through autonomous robots. By combining real-time object recognition with robotic manipulation, Wu’s research demonstrates how affordable, vision-guided robots can improve quality of life for seniors and enhance food supply chain safety. His work reflects a growing trend toward socially assistive robotics and human-centered automation, positioning him as an emerging voice in applied computer vision and human-robot collaboration.