Papers
3
Total Citations
15
H-Index
2
About
Changqi Li is a pioneering researcher whose work spans the frontiers of robotic construction, intelligent control, and surgical robotics. His most impactful contributions center on three transformative areas: cable-driven robotic systems for autonomous 3D printing, neural network-based robot learning control, and machine learning for surgical skill assessment. In his landmark 2024 study, Li developed and experimentally validated a novel deployable cable-driven construction 3D printer (CDCP), demonstrating its potential for large-scale autonomous construction—including lunar surface shelters—with a remarkable workspace and load-to-weight ratio. This work, already garnering 7 citations, positions him at the forefront of extraterrestrial construction robotics. Earlier, in 1997, Li introduced a pioneering recurrent neural network (RNN)-based inverse model controller for robots with unknown dynamics, a foundational contribution that continues to influence adaptive control research with 7 citations. Most recently, his 2025 preliminary study on sEMG-based motion recognition for robotic surgery training, employing machine learning and variable-length sliding windows, opens new pathways for objective skill assessment in surgical education. Li’s work uniquely bridges theoretical control innovation with practical, high-impact applications in construction and medicine.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robot learning control based on recurrent neural network inverse model7 citations · 1997
- 3