Yingli Xu

Huazhong University of Science and Technology

Papers

Total Citations

H-Index

About

Yingli Xu’s research lies at the intersection of human-robot interaction (HRI) and intelligent robotic perception, with a focus on enabling robots to perform complex, human-like tasks. Her most cited work, “Robots Learn to Write via Human–Robot Interaction” (2020, 2 citations), tackles the challenging problem of teaching robots to write through a seamless integration of perception, learning, and control. This paper develops a robotic handwriting system that learns directly from human demonstration, advancing the field of interactive robot learning. In her second highly cited study, “Target Detection in NAO Robot Golfing” (2021, 2 citations), Xu addresses the critical task of identifying and tracking small balls under difficult visual conditions—such as uneven lighting and blurred borders—by combining the random Hough transform with Kalman filtering. This work demonstrates her ability to solve real-world perception challenges in dynamic environments. Though early in her career, Xu’s contributions to HRI and robotic perception are already recognized for their practical impact, laying groundwork for more autonomous and interactive robotic systems. Her research is particularly valuable for students and engineers interested in bridging human teaching with robotic skill acquisition.