Chia-Ching Hung
Papers
2
Total Citations
42
H-Index
2
About
Chia-Ching Hung is a leading researcher in the intersection of artificial intelligence, fuzzy systems, and humanoid robotics. Their primary research areas include reinforcement learning, adaptive control, and sensor fusion for bipedal locomotion. Hung’s major contributions center on developing intelligent gait pattern controllers that enable humanoid and biped robots to walk with greater stability and adaptability. Notably, their 2020 paper, “Fuzzy Double Deep Q-Network-Based Gait Pattern Controller for Humanoid Robots,” pioneered the combination of adaptive-network-based fuzzy inference systems (ANFIS) with double deep Q-networks (DDQN), creating a novel fuzzy DDQN (FDDQN) framework. This work, with 23 citations, allows robots to dynamically adjust their gaits in real time. In another highly cited study (19 citations), “Sequential Sensor Fusion-Based Real-Time LSTM Gait Pattern Controller for Biped Robot,” Hung integrated inertial measurement units (IMUs) and pressure sensors with long short-term memory (LSTM) networks to model walking corrections. These contributions have significantly advanced the field of legged robotics, providing robust, data-driven solutions for stable locomotion. Hung’s work is essential reading for researchers and students interested in intelligent control, fuzzy logic, and autonomous robot navigation.
Research Focus
Key Achievements
Top Papers
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