Wei-Cyuan Yang
Papers
3
Total Citations
24
H-Index
3
About
Wei-Cyuan Yang is a rising researcher in intelligent robotics, specializing in the intersection of reinforcement learning, bio-inspired optimization, and motion control for legged systems. His work focuses on developing adaptive decision-making frameworks that enable humanoid and multi-legged robots to achieve stable, autonomous locomotion in dynamic environments. Yang’s most impactful contribution is an intelligent proximal-policy-optimization (PPO)-based decision-making system for humanoid robots, which has garnered 12 citations since 2023 and demonstrates how deep reinforcement learning can enhance real-time balance and gait selection. He further advanced the field with a machine learning-based motion training approach applicable to both multilegged and bipedal robots (8 citations, 2024), offering a unified framework for diverse robotic morphologies. His recent work on an artificial rabbits optimization–based motion balance system for bipedal impact recovery (4 citations, 2024) exemplifies his creative use of swarm intelligence to solve critical stability challenges. While early in his career, Yang’s research is notable for its practical focus on robust, real-world robot control, bridging theoretical algorithms with deployable solutions. His growing citation record signals emerging influence in the robotics community, particularly for students and engineers seeking data-driven approaches to legged locomotion and fall recovery.
Research Focus
Key Achievements
Top Papers
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