Zicheng Fang

Papers

Total Citations

H-Index

About

Zicheng Fang is a robotics researcher whose work focuses on autonomous manipulation and perception for industrial applications. His key research areas include vision-guided robotic control, force-based interaction, and automated surface coating. Fang’s major contribution lies in developing cost-effective methods that enable general-purpose robots to perform complex, precision tasks traditionally requiring specialized equipment. His most cited work, "Vision and force based autonomous coating with rollers" (2020), introduces a novel approach to structural painting—a field where coating rollers offer advantages over brushes and sprayers in paint thickness, color consistency, and customizability. By integrating visual feedback with force sensing, Fang’s method allows robots to autonomously navigate and coat surfaces, significantly reducing the need for human labor and expensive custom machinery. While his citation count is still growing (3 citations for this paper), the work demonstrates foundational impact in bridging perception and physical interaction for practical automation. Fang’s research is particularly notable for its emphasis on affordability and accessibility, aiming to bring robotic solutions to small-scale and do-it-yourself applications. His achievements highlight a promising trajectory in making autonomous coating systems more versatile and widely adoptable.