Papers
4
Total Citations
40
H-Index
3
About
Kin-Man Lam is a researcher whose work spans robotics, manufacturing, and medical technology, with a focus on intelligent systems and precision control. His key research areas include deep learning for robotic disassembly, compliance control for polishing mechanisms, and self-supervised depth completion for autonomous systems. Lam’s major contributions are exemplified by his 2023 paper on predictive exposure control for vision-based robotic disassembly, which integrates deep learning and predictive learning to enhance automation—a work that has garnered 28 citations, reflecting its impact on efficient and adaptive robotics. He has also advanced active compliance smart control strategies for bonnet polishing, addressing limitations in load capacity and positioning for ultraprecision processes. Additionally, his 2020 study on self-supervised depth completion with attention-based loss tackles challenges in dense depth prediction for robotics and autonomous driving, though with fewer citations. Notably, Lam has contributed to medical robotics through a comparative study of laparoscopic and robot-assisted radical prostatectomy, evaluating perioperative and functional outcomes. His interdisciplinary work demonstrates a commitment to bridging theoretical advances with practical applications in manufacturing and healthcare.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Self-supervised depth completion with attention-based loss3 citations · 2020
- 4