Linyuan Guo
Papers
2
Total Citations
58
H-Index
2
About
Linyuan Guo is a robotics and control systems researcher whose work centers on intelligent autonomous systems and model-free optimization techniques. Guo has made notable contributions to the field of two-wheeled self-balancing robots (TWSBRs), a class of dynamically unstable systems that present significant challenges for conventional control approaches. Recognizing the limitations of traditional optimal control methods — which typically demand precise mathematical models of the system — Guo pioneered the application of reinforcement learning, particularly Q-learning, as a model-free alternative capable of achieving robust stabilization across linear, tilt, and yaw motion axes. Their most influential publication, "Optimal Control of a Two-Wheeled Self-Balancing Robot by Reinforcement Learning" (2020), has garnered 49 citations, reflecting meaningful uptake within the robotics and control engineering communities. A closely related companion study further elaborates on the Q-learning framework, accumulating an additional 9 citations. Together, these works demonstrate Guo's focused commitment to bridging reinforcement learning theory with real-world robotic control challenges. For students and researchers working at the intersection of machine learning and autonomous systems, Guo's research offers a compelling, practically grounded perspective on next-generation intelligent control strategies.
Research Focus
Key Achievements
Top Papers
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