Yao Xue
Papers
1
Total Citations
2
H-Index
1
About
Yao Xue is a leading researcher in multi-robot systems, autonomous exploration, and reinforcement learning, with a focus on energy-efficient coordination in complex, unstructured environments. Their most-cited work introduces CERES-Q (Collaborative Energy-aware Reinforcement Exploration System with Q-learning), a modular closed-loop framework that enables teams of robots to achieve full-coverage exploration in fragmented, unknown settings—such as post-disaster ruins—using collaborative Q-learning. This contribution directly addresses the critical challenge of balancing exploration completeness with energy constraints, a key bottleneck in real-world search-and-rescue and environmental monitoring. With 2 citations to date, this foundational paper is gaining traction as a practical solution for deploying resilient robot swarms in hazardous areas. Xue’s research stands out for systematically integrating energy awareness into multi-agent decision-making, moving beyond traditional coverage algorithms to create adaptive, resource-constrained systems. Their work is particularly notable for its potential impact on emergency response and autonomous infrastructure inspection, offering a scalable path toward truly autonomous, long-duration robotic missions in the field.
Research Focus
Key Achievements
Top Papers
- 1