Zhifang Wu

Papers

Total Citations

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About

Zhifang Wu’s research lies at the intersection of human-robot interaction, computer vision, and intelligent gaming systems, with a particular focus on enabling robots to engage in complex, real-world social activities. Their most notable work, “A Human-Robot Interactive Mahjong Playing System Based on Visual Recognition Using a Convolutional Neural Network” (2021), tackles the formidable challenge of incomplete information games—far more complex than Go or chess. By integrating CNN-based visual recognition with robotic manipulation, Wu created a system that allows a robot to perceive tiles, make strategic decisions, and physically play the game alongside human partners. This contribution not only advances service robotics but also demonstrates how deep learning can bridge the gap between abstract AI and tangible, interactive experiences. While the work has garnered 2 citations to date, its novelty lies in its interdisciplinary approach, merging game theory, robotics, and visual perception. Wu’s research opens doors for more natural human-robot collaboration in entertainment, education, and assistive contexts, marking a meaningful step toward robots that can participate in culturally rich, dynamic social settings.