Xiaobei Cheng
Papers
2
Total Citations
25
H-Index
2
About
Dr. Xiaobei Cheng is a pioneering researcher in multi-robot systems, with a primary focus on hierarchical reinforcement learning and cooperative control in complex, communication-constrained environments. Her most cited work, "Multi-robot Cooperation Based on Hierarchical Reinforcement Learning" (2007, 23 citations), established foundational methods for enabling robots to learn coordinated behaviors through structured decision-making. Building on this, her 2010 study on "Multi-robot hierarchical reinforcement learning based on semi-Markov games" introduced a novel approach that integrates game theory to address critical challenges in underwater and other environments where communication failures are common. This work demonstrated how robots can maintain effective cooperation even with limited information exchange, advancing the field's understanding of robust multi-agent systems. Dr. Cheng's contributions are particularly notable for bridging reinforcement learning and game-theoretic frameworks, offering practical solutions for real-world applications such as underwater exploration and disaster response. Her research continues to influence the development of adaptive, resilient robotic teams.
Research Focus
Key Achievements
Top Papers
- 1Multi-robot Cooperation Based on Hierarchical Reinforcement Learning23 citations · 2007
- 2