Chien-Hsin Chang
Papers
3
Total Citations
48
H-Index
3
About
Chien-Hsin Chang is a leading researcher in the intersection of artificial intelligence and robotics, with a primary focus on humanoid and bipedal locomotion control. His work centers on developing intelligent, real-time gait pattern controllers that enable robots to walk with greater stability and adaptability. Chang’s major contributions include pioneering the integration of fuzzy logic with deep reinforcement learning, as demonstrated in his highly cited paper on the Fuzzy Double Deep Q-Network (FDDQN) for humanoid robots (23 citations). He also advanced sensor fusion techniques by combining inertial measurement units and pressure sensors with Long Short-Term Memory (LSTM) networks, creating a sequential sensor fusion-based gait controller (19 citations). In a notable interdisciplinary achievement, Chang applied cognitive psychology concepts from Daniel Kahneman’s *Thinking, Fast and Slow* to design a deep belief network learning algorithm for a humanoid pitching game (6 citations). His work has been instrumental in bridging the gap between theoretical AI models and practical robotic applications, making him a key figure in the development of more autonomous and intelligent robotic systems.
Research Focus
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