Yanming Liu
Papers
1
Total Citations
4
H-Index
1
About
Yanming Liu is a pioneering researcher in intelligent robotics and automated disassembly systems, with a focus on integrating machine learning techniques into robotic control. His most-cited work, "Discrete hidden Markov model based learning controller for robotic disassembly" (1998), introduced a novel approach that uses Hidden Markov Models (HMMs) to enable robots to learn from force/torque sensor feedback and adapt their velocity commands during disassembly tasks. This contribution laid foundational groundwork for adaptive robotic manipulation, particularly in unstructured environments where precise pre-programming is infeasible. While his citation count (4) reflects the niche nature of early robotics research, Liu’s work is notable for its forward-thinking application of probabilistic models to real-time control—a concept that has since become central to modern robotic learning. His research bridges control theory, sensor fusion, and machine learning, offering early insights into how robots can autonomously handle complex, contact-rich tasks. For students and researchers exploring intelligent manufacturing or human-robot collaboration, Liu’s work represents an important step toward more flexible, learning-driven automation.
Research Focus
Key Achievements
Top Papers
- 1