Qingyang Lyu
Papers
1
Total Citations
4
H-Index
1
About
Qingyang Lyu is a researcher advancing the frontier of autonomous robot navigation in complex, dynamic environments. His primary research areas lie at the intersection of deep reinforcement learning, computer vision, and mobile robotics, with a focus on enabling robots to operate safely and efficiently among human crowds. Lyu’s most notable contribution is his work on visual target-driven navigation, where he addresses the critical challenge of limited field-of-view sensors in crowded settings. His 2025 paper, "Visual Target-Driven Robot Crowd Navigation with Limited FOV Using Self-Attention Enhanced Deep Reinforcement Learning," proposes an innovative self-attention mechanism that allows robots to better perceive and predict pedestrian movements, overcoming the shortcomings of traditional SLAM-based approaches. This work, already garnering 4 citations shortly after publication, demonstrates his ability to tackle real-world robotic problems with cutting-edge AI techniques. Lyu’s research holds significant promise for applications in service robotics, autonomous delivery, and human-robot interaction, marking him as an emerging voice in the field of intelligent navigation systems.
Research Focus
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Top Papers
- 1