Quanwei Zheng
Papers
1
Total Citations
5
H-Index
1
About
Quanwei Zheng is a researcher in robotics and artificial intelligence, with a primary focus on bipedal locomotion and reinforcement learning. His most notable contribution is the development of a novel reinforcement learning method to stabilize biped walking on rough terrain, as detailed in his 2015 paper. This work addresses the challenge of continuous state and action spaces in biped walking by employing neural networks within an actor-critic learning framework to approximate optimal policies. While his most cited paper has garnered 5 citations, it represents a foundational step in applying advanced machine learning techniques to real-world robotic control problems. Zheng’s research sits at the intersection of control theory and AI, aiming to create more adaptive and robust walking robots capable of navigating uneven environments. His work is particularly relevant for students and researchers interested in combining reinforcement learning with physical systems, offering insights into how neural networks can enhance the stability and performance of autonomous robots in challenging terrains.
Research Focus
Key Achievements
Top Papers
- 1Biped walking on rough terfrain using reinforcement learning5 citations · 2015