Qingyuan Zheng
Papers
7
Total Citations
53
H-Index
5
About
Qingyuan Zheng is an emerging robotics researcher whose work centers on the control and autonomous navigation of single-track two-wheeled (STTW) robots and reaction wheel bicycle robots — two classes of underactuated mobile systems prized for their compact form and ability to traverse constrained environments. His research sits at the productive intersection of reinforcement learning and advanced control theory, demonstrating how data-driven methods can overcome the inherent instability and model uncertainty challenges these platforms present. Zheng's most recognized contribution — a continuous reinforcement learning framework for ramp jump control of STTW robots (2021, 15 citations) — tackled one of the field's most demanding maneuvers, requiring simultaneous balance maintenance and speed adaptation across varying terrain profiles. Subsequent work extended this foundation to curved pavement scenarios, integrating online reinforcement learning with terminal sliding mode control to handle matched and mismatched disturbances robustly (2022, 12 citations). His series-parallel learning architecture for reaction wheel bicycle robots (2023, 10 citations) further illustrates his systematic approach to bridging model-based and learning-based paradigms. Collectively accumulating over 50 citations within just a few years, Zheng's growing body of work establishes him as a notable contributor to intelligent autonomous robot control in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7