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About

Xingyang Liu is a leading researcher in biped robotics, specializing in bio-inspired control systems, reinforcement learning, and intelligent optimization for humanoid locomotion. Their major contributions center on developing novel control frameworks that mimic human motor strategies to enhance walking stability and adaptability in biped robots. Liu pioneered the Human-Simulated Fuzzy (HF) membrane control system, which integrates fuzzy logic with membrane computing for precise joint angle regulation, earning 19 citations. They further advanced the field with an Entropy-Weighted Numerical Gradient Optimization Spiking Neural System, which efficiently tackles multi-objective, multi-parameter controller optimization—a breakthrough cited 18 times. Liu also introduced a constrained Deep Deterministic Policy Gradient (DDPG) algorithm for safe, accurate gait optimization, and the Human-Simulated Intelligent Walking Control (HIWC) scheme, which outperforms traditional proportional-derivative controllers. With a growing citation impact across these works, Liu’s research bridges computational intelligence and mechanical engineering, offering practical solutions for stable, human-like walking in real-world biped robots. Their work is essential reading for students and researchers in robotics, control theory, and artificial intelligence.