Cheng-Hung Lin

National Taiwan Normal University, Chung Hua University

Papers

Total Citations

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About

Cheng-Hung Lin is a robotics researcher whose work focuses on making robotic systems more intelligent, adaptable, and autonomous. His primary research areas include vision-based learning, robotic manipulation, and sensor-based control systems. Lin’s most impactful contribution is his work on "Vision-Based Learning from Demonstration System for Robot Arms" (2022, 15 citations), which addresses a critical challenge in industrial robotics: the time-consuming process of reprogramming robots for new tasks. By enabling robots to learn from human demonstrations using visual input, Lin’s system significantly reduces deployment time and increases operational flexibility. He has also advanced 3D object pose estimation through "Iterative Pose Refinement for Object Pose Estimation Based on RGBD Data" (2020, 5 citations), improving accuracy for applications in robotics and augmented reality. Additionally, Lin explored intelligent control systems in "Mobile robot-based antenna tracking system on RPV using intelligent neural controller" (2017, 2 citations), comparing traditional PI controllers with neural network approaches for antenna stabilization. His work bridges the gap between theoretical control methods and practical robotic applications, contributing to the development of more autonomous and efficient robotic systems for real-world deployment.