Xiaoyun Lei
Papers
1
Total Citations
165
H-Index
1
About
Xiaoyun Lei is a leading researcher in artificial intelligence and robotics, with a primary focus on deep reinforcement learning and autonomous navigation. Her most impactful contribution is the pioneering application of Double Deep Q-Network (DDQN) to dynamic path planning in unknown environments, a breakthrough that directly addressed a long-standing challenge for mobile robots. Her seminal 2018 paper on this topic has garnered 165 citations, underscoring its influence on both theoretical AI and practical robotics. By designing novel reward and punishment functions and optimized training mechanisms, Lei’s work enables robots to make real-time, intelligent decisions without pre-mapped environments, advancing the field of autonomous systems. Her research bridges the gap between reinforcement learning algorithms and real-world robotic applications, making her a key figure in the development of adaptive, self-learning navigation technologies. Lei’s contributions continue to inspire new approaches in autonomous driving, drone navigation, and industrial robotics.
Research Focus
Key Achievements
Top Papers
- 1