Wen-Yue Shan
Papers
1
Total Citations
8
H-Index
1
About
Wen-Yue Shan is a robotics and artificial intelligence researcher whose work focuses on reinforcement learning and real-time control systems for autonomous robots. His most significant contribution lies in the development of the eXtended Classifier System for Real-input and Real-output (XCSRR), an improved reinforcement learning framework designed to handle stability control problems in biped robots operating in fully real-valued environments. This work, published in 2015, has garnered 8 citations and addresses a critical challenge in robotics: enabling machines to learn and adapt their locomotion in continuous, dynamic settings without the need for discretized inputs or outputs. Shan’s approach bridges the gap between theoretical machine learning and practical robotic control, offering a robust solution for real-time stability in bipedal systems. His research is particularly relevant for students and engineers interested in adaptive control, evolutionary computation, and the intersection of AI with physical robotics. By advancing classifier systems for real-world applications, Shan has laid groundwork for more autonomous and resilient robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1