Hao-Lun Huang
Papers
3
Total Citations
23
H-Index
3
About
Hao-Lun Huang is a rising force in industrial robotics, specializing in the dynamic identification and intelligent control of robot manipulators. His research bridges advanced system identification, evolutionary algorithms, and edge-deployed machine learning to enhance the precision and autonomy of industrial robotic arms. Huang’s most impactful work introduces a novel closed-loop input error (CLIE) approach, leveraging evolutionary algorithms to estimate dynamic parameters directly from joint torque residuals—a method that has already garnered 14 citations since its 2024 publication. He also developed a rapid recurrent neural network (RNN)-based test for ensuring physical feasibility of base parameters, a critical step for reliable dynamic modeling. Looking toward the future of smart manufacturing, Huang’s 2025 study integrates tiny machine learning (TinyML) and edge computing for real-time multi-object recognition, enabling robotic arms to perceive and adapt to their environment without cloud dependency. With a growing citation record and contributions that directly address real-world industrial challenges—from calibration to cognitive automation—Huang is establishing himself as a key innovator in the next generation of adaptive, intelligent robotics.
Research Focus
Key Achievements
Top Papers
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