Yaofang Zhang
Papers
1
Total Citations
73
H-Index
1
About
Yaofang Zhang is a leading researcher in interactive robotics and augmented reality (AR), with a focus on bridging human intuition and machine intelligence. Her work centers on enabling robots to acquire, diagnose, and update interpretable knowledge through intuitive human-robot interaction. In her highly cited 2018 paper, "Interactive Robot Knowledge Patching Using Augmented Reality," Zhang introduced a groundbreaking AR approach using the Microsoft HoloLens to teach robots complex tasks, such as opening bottles, by learning a Temporal And-Or graph (T-AOG) from human demonstration. This work, with 73 citations, addresses critical challenges in robot learning—diagnosing errors, teaching new skills, and patching knowledge gaps—without requiring programming expertise. Zhang’s contributions have significant implications for manufacturing, healthcare, and assistive robotics, where flexible, real-time knowledge updates are essential. Her research exemplifies how AR can democratize robot programming, making it accessible to non-experts while maintaining high interpretability. With a growing citation impact, Zhang is recognized for advancing human-robot collaboration and for her innovative use of mixed reality to create more adaptive, transparent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Interactive Robot Knowledge Patching Using Augmented Reality73 citations · 2018