Papers
3
Total Citations
17
H-Index
3
About
Guang Yan is a pioneering researcher at the intersection of robotics and traditional art, specializing in robotic Chinese calligraphy. His work addresses the fundamental challenge of teaching machines to replicate the nuanced, expressive brush strokes of Chinese characters—a task requiring both precise physical modeling and adaptive learning. Yan’s major contributions center on the development of novel brush stroke models, most notably the Composite-Curve-Dilation Brush Stroke Model (CCD-BSM), which bridges the gap between end-to-end deep learning methods and physics-based stroke generation. His 2022 paper on CCD-BSM, with 6 citations, introduced a composite-curve-dilation approach that captures the subtle pressure and trajectory variations of a real brush. This was extended in 2023 with the CCD-BSMG (Generator), a system that can learn from a large dataset of strokes collected by a robotic arm, enabling more autonomous and realistic calligraphy generation. With over 17 total citations across his core papers, Yan’s work is foundational for researchers exploring how to combine data-driven learning with physical constraints in robotic art. His research not only advances human-robot interaction but also preserves and reimagines cultural heritage through technology.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3