Lin-Han Chen

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About

Lin-Han Chen is a leading researcher at the intersection of artificial intelligence, fuzzy systems, and humanoid robotics. His primary research areas include reinforcement learning, adaptive control, and intelligent gait pattern generation for bipedal locomotion. Chen’s most notable contribution is the development of the Fuzzy Double Deep Q-Network (FDDQN), a pioneering framework that synergizes the adaptive-network-based fuzzy inference system (ANFIS) with the double deep Q-network (DDQN). This innovation enables humanoid robots to autonomously learn and produce stable, adaptive walking patterns in complex environments, addressing a critical challenge in robotics. His seminal 2020 paper on this topic has garnered 23 citations, reflecting its influence in advancing robot control strategies. Chen’s work bridges the gap between model-free reinforcement learning and interpretable fuzzy logic, offering a robust solution for real-time motion planning. By integrating neural and fuzzy paradigms, he has opened new pathways for creating more resilient and intelligent autonomous systems. His achievements are particularly impactful for researchers exploring human-robot interaction and adaptive locomotion, positioning him as a key figure in the evolution of next-generation robotic controllers.