Papers

15

Total Citations

162

H-Index

6

About

Wei-Cheng Jiang is a robotics and machine learning researcher whose work sits at the intersection of reinforcement learning, robot locomotion, and intelligent control systems. Best known for his pioneering contributions to biped robot locomotion, Jiang demonstrated that dynamic walking and balance control could be achieved through Q-learning without requiring any prior knowledge of a robot's dynamic model — a landmark insight reflected in his most-cited work, "Gait Balance and Acceleration of a Biped Robot Based on Q-Learning" (2016, 48 citations). Building on this foundation, his 2017 study on motion segmentation and imitation learning (34 citations) tackled the challenging problem of transferring human motion trajectories to humanoid robots while preserving dynamic stability. Beyond bipedal locomotion, Jiang has made notable contributions to image-based visual servoing, developing fuzzy CMAC and reinforcement learning-based controllers for robotic arms, and has explored multi-agent policy sharing and inverse reinforcement learning to automate reward function design. His early work on CMAC-Q-Learning Dyna agents reflects a sustained commitment to accelerating learning efficiency in complex robotic systems. Across more than a decade of research, Jiang's cumulative contributions have shaped how autonomous robots learn, balance, and adapt in dynamic real-world environments.

Research Focus

Key Achievements

6
H-Index
15
Papers
162
Total Citations
11
Avg Citations/Paper
🏆 Most Cited Paper
Gait Balance and Acceleration of a Biped Robot Based on Q-Learning
48 citations · 2016
📈 Most Prolific Year: 2013 (4 Papers)
🤝 Key Collaborators: 18
🏛 Institutions: National Sun Yat-sen University, Tunghai University, National Formosa University, National Chung Cheng University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 15 days ago