Papers
8
Total Citations
96
H-Index
5
About
Xuefang Li is a robotics researcher whose work spans bio-inspired locomotion, human-robot interaction, and agricultural automation. Her research focuses on motion control, iterative learning control (ILC), and impedance adaptation for robotic systems operating in dynamic, unstructured environments. She has made significant contributions to the development of data-driven control strategies for robotic fish, achieving precise speed and trajectory tracking through model-free learning approaches (e.g., 49 citations for her 2015 work). In the domain of physical human-robot interaction, Li proposed a robotic impedance learning framework for robot-assisted training, addressing the open problem of context-aware parameter adaptation (14 citations, 2019). She has also advanced soft robotics with ILC-based motion control for circular crawling robots using dielectric elastomer actuators (12 citations, 2019). Most recently, Li extended her expertise to smart agriculture, developing VBP-YOLO-prune, a lightweight apple detection model robust to variable weather conditions (9 citations, 2025). Her work is characterized by a strong integration of theory and experiment, with demonstrated impact through over 90 total citations across her most-cited papers. Li’s research continues to push the boundaries of adaptive, learning-based control for next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
- 1A data-driven motion control approach for a robotic fish49 citations · 2015
- 2Robotic Impedance Learning for Robot-Assisted Physical Training14 citations · 2019
- 3
- 4
- 5
- 6
- 7
- 8