Papers
12
Total Citations
97
H-Index
6
About
Yongjia Zhao is a pioneering researcher at the intersection of robotics, digital twin technology, and precision manufacturing, with a particular focus on the assembly of flexible printed circuits (FPCs) for consumer electronics. Her work addresses the critical challenge of enabling robots to perform delicate, high-precision tasks—such as connecting tiny FPC components in mobile phones—that have traditionally required human dexterity. Zhao’s major contributions include developing a digital twin-driven measurement system for robotic FPC assembly (21 citations), which enhances trustworthiness in automated manufacturing, and creating a digital-twin-assisted skill learning framework for 3C assembly tasks (13 citations). She has also pioneered the use of haptic information for accurate FPC position identification (7 citations) and demonstrated autonomous pollen delivery to forsythia pistils using a robot arm (23 citations), showcasing cross-domain applications of precision robotics. Her innovative work on real-time assembly activity recognition through virtual reality demonstrations (10 citations) and reinforcement learning guided by vision-language models (3 citations) further underscores her impact. With over 90 total citations, Zhao’s research is instrumental in advancing robotic autonomy for complex, small-scale assembly operations, promising significant reductions in labor costs and improvements in manufacturing efficiency.
Research Focus
Key Achievements
Top Papers
- 1
- 2Digital Twin Driven Measurement in Robotic Flexible Printed Circuit Assembly21 citations · 2023
- 3Digital-Twin-Assisted Skill Learning for 3C Assembly Tasks13 citations · 2024
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- 6An efficient locomotion strategy for six-strut tensegrity robots6 citations · 2017
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- 10Tensegrity robot dynamic simulation and kinetic strategy programming3 citations · 2016