Yuqi Fang
Papers
2
Total Citations
12
H-Index
2
About
Yuqi Fang is a researcher whose work sits at the intersection of computer vision, robotics, and human-robot interaction, with a particular focus on enabling autonomous systems to operate in complex, real-world environments. Her most notable contribution is the creation of a large-scale, publicly available dataset specifically designed for benchmarking elevator button segmentation and character recognition—a critical, yet often overlooked, challenge for autonomous robots performing inter-floor navigation. This work, published in 2021 and accumulating over a dozen citations, directly addresses a key bottleneck in service robotics: the need for robots to independently operate existing infrastructure without human assistance or costly retrofitting. By providing a standardized evaluation framework, Fang’s dataset has become a foundational resource for researchers aiming to improve a robot’s ability to perceive and interact with its surroundings. Her research is particularly timely, addressing the surge in demand for contactless service robots during the COVID-19 pandemic. Fang’s contributions are paving the way for more capable, self-sufficient robots that can seamlessly navigate human-centric spaces, marking her as a promising voice in the field of autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2