Papers
3
Total Citations
59
H-Index
3
About
Zhiqi Cao is a leading researcher in the field of robotic assembly and human-robot skill transfer, with a focus on programming by demonstration (PbD) and learning from demonstration (LfD). His work addresses the critical challenge of automating complex assembly tasks, particularly for 3C (computer, communication, and consumer electronics) products, by enabling robots to learn directly from human workers. Cao’s most cited paper (2021, 36 citations) provides a rigorous performance evaluation of optical motion capture sensors for capturing human assembly motions, establishing a foundational methodology for translating human actions into robot commands. His 2019 work (15 citations) introduces an intuitive method for robots to learn 3C assembly skills from human demonstrations, directly tackling the urgent need for automation in labor-intensive industries. A subsequent 2019 paper (8 citations) advances this approach by incorporating local human corrections during robot execution, enhancing the precision and adaptability of learned assembly policies. Through these contributions, Cao has demonstrated how PbD can bridge the gap between human expertise and robotic automation, offering practical solutions for industrial assembly lines. His research continues to shape the development of more intuitive, efficient, and human-centric robot programming methods.
Research Focus
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