Papers
1
Total Citations
6
H-Index
1
About
Qiong Gu is a researcher advancing intelligent control systems for autonomous mobile robotics, with a primary focus on reinforcement learning and multi-attribute decision-making. Her most-cited work, "An Experience Aggregative Reinforcement Learning With Multi-Attribute Decision-Making for Obstacle Avoidance of Wheeled Mobile Robot" (2020, 6 citations), addresses a critical bottleneck in conventional RL methods: the difficulty robots face in selecting appropriate actions during motion control. Gu’s key contribution lies in developing an experience aggregative framework that integrates multi-attribute decision-making into reinforcement learning, enabling wheeled mobile robots to navigate complex environments more effectively by evaluating multiple action criteria simultaneously. This approach enhances the robots’ ability to avoid obstacles through improved action selection, moving beyond the performance limitations of standard RL algorithms. While her citation count is still growing, her work represents a meaningful step toward more adaptive and intelligent robotic systems. Gu’s research sits at the intersection of control theory, machine learning, and autonomous navigation, offering practical solutions for real-world robotic applications. Her innovative methodology provides a foundation for future studies in experience-based learning and decision fusion in robotics.
Research Focus
Key Achievements
Top Papers
- 1