Jiusen Guo
Papers
2
Total Citations
16
H-Index
2
About
Jiusen Guo is a researcher advancing the field of robotic manipulation through intelligent visual servoing and machine learning. His work focuses on developing adaptive, uncalibrated control systems that enable robot manipulators to operate with precision even when camera parameters are unknown or changing. Guo’s major contributions include the integration of Kalman filtering with mixed-kernel online sequential extreme learning machines, a novel approach that significantly improves real-time tracking and control performance. His 2023 paper on this method has already garnered 11 citations, reflecting its growing influence in the robotics community. Additionally, his 2022 study on random vector functional link networks with L21 norm regularization introduced a robust framework for visual servo control under feature constraints, earning 5 citations. These works demonstrate Guo’s commitment to bridging theoretical machine learning with practical robotic applications, offering scalable solutions for dynamic environments. His research is particularly valuable for students and engineers seeking to enhance robot autonomy without costly calibration, making him a notable contributor to the next generation of intelligent manufacturing and automation systems.
Research Focus
Key Achievements
Top Papers
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