Guoliang Fan
Papers
2
Total Citations
5
H-Index
2
About
Guoliang Fan is a researcher at the intersection of artificial intelligence, multi-agent systems, and robotics, with a focus on developing intelligent decision-making and control frameworks. His most notable contribution is the introduction of an **actor-critic approach for controlling Large Language Model (LLM)-based agents in large-scale decision-making** (2023). This work directly addresses the critical challenges of LLM hallucination and multi-agent coordination, offering a scalable solution for complex, real-world MAS problems. In the domain of robotics, Fan has advanced **motion capture and human tracking** by designing a **virtual Mecanum wheeled robot ROS simulator** (2024). This simulator enables multi-view and self-following motion capture, overcoming control limitations of traditional differential-drive robots for applications in clinical gait analysis and rehabilitation. While his work is recent, with top papers accumulating over 5 citations, his contributions are pioneering in merging LLM reasoning with multi-agent control and in creating practical robotic tools for healthcare. Fan’s research is particularly relevant for students and engineers interested in embodied AI, cooperative robotics, and the deployment of LLMs in dynamic, multi-agent environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2