Pengyu Yue
Papers
1
Total Citations
27
H-Index
1
About
Pengyu Yue is a researcher at the forefront of intelligent robotics and autonomous systems, with a primary focus on deep reinforcement learning for mobile robot navigation. Their seminal work, "Experimental Research on Deep Reinforcement Learning in Autonomous Navigation of Mobile Robot" (2019, 27 citations), addresses a critical challenge in robotics: enabling a robot to navigate from its current position to a desired destination using only visual observations, without requiring a pre-built environmental map. By leveraging Deep Q Networks (DQN), Yue demonstrated how reinforcement learning can empower robots to make real-time, adaptive decisions in unfamiliar settings—a breakthrough that reduces dependency on costly mapping infrastructure. This contribution has been influential in advancing end-to-end learning paradigms for autonomous navigation, inspiring further research in vision-based control and model-free robotics. Yue’s work bridges the gap between theoretical reinforcement learning algorithms and practical robotic applications, offering a scalable pathway for deploying intelligent agents in dynamic, unstructured environments. Their research continues to shape how robots perceive and interact with the world, with implications for service robotics, autonomous vehicles, and industrial automation.
Research Focus
Key Achievements
Top Papers
- 1