Yingnian Wu

Beijing Information Science & Technology University

Papers

Total Citations

H-Index

About

Yingnian Wu’s research lies at the intersection of robotics, human–machine interaction, and intelligent control, with a focus on enabling robots to perceive, follow, and interact with dynamic environments. His major contributions include developing a body-following wheeled robot system that uses Kinect-based optical flow and gesture recognition for natural human–robot coordination—work that has direct applications in smart factories. He has also advanced deep reinforcement learning for moving target shooting control, addressing the challenge of robot control in dynamic settings. In the domain of biomimetic robotics, Wu designed a single-joint robotic fish inspired by the boxfish, integrating camera and infrared sensors for real-time moving target tracking. Though his most-cited papers currently hold modest citation counts (ranging from 2 to 5), they represent foundational steps in practical, sensor-driven robotic autonomy. His work demonstrates a clear trajectory from perception-based human following to adaptive, learning-based control—making his research particularly relevant for students and engineers interested in deploying intelligent robots in unstructured, real-world environments.