Papers
33
Total Citations
529
H-Index
13
About
Ping-Huan Kuo is a leading researcher at the intersection of robotics, artificial intelligence, and human-robot interaction. His work focuses on enabling robots—from humanoids to robotic arms—to perceive, learn, and act intelligently in dynamic environments. Kuo’s major contributions include developing emotion recognition systems that allow humanoid robots to interpret facial expressions using CNN-LSTM models (106 citations), and pioneering optimization algorithms like 3D adaptive PSO for IoT-based automated packaging (52 citations). He has also advanced gait control for biped robots through bio-inspired algorithms such as artificial bee colony and fuzzy double deep Q-networks, enabling more natural and adaptive locomotion. With over 350 total citations across his top publications, Kuo’s research demonstrates high impact in both theoretical optimization and practical robotics. His notable achievements include integrating sensor fusion with LSTM networks for real-time gait control and designing intelligent strategies for robotic arm manipulation. For students and researchers, Kuo’s work offers a compelling model of how computational intelligence can bridge the gap between robotic hardware and human-centric applications.
Research Focus
Key Achievements
Top Papers
- 1CNN and LSTM Based Facial Expression Analysis Model for a Humanoid Robot106 citations · 2019
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