Papers
1
Total Citations
20
H-Index
1
About
Pei Xu is a researcher working at the intersection of deep reinforcement learning and autonomous mobile robotics. Their most recognized contribution focuses on map-based obstacle avoidance for mobile robot navigation, where they developed a deep reinforcement learning framework that enables robots to intelligently interpret environmental maps and make real-time navigation decisions without colliding with obstacles. This work, published in 2021, has garnered 20 citations, reflecting growing interest from the robotics and AI communities in data-driven approaches to autonomous navigation. By leveraging reinforcement learning — a paradigm where agents learn optimal behaviors through trial and reward — Xu's research addresses one of the fundamental challenges in robotics: enabling machines to navigate complex, dynamic environments safely and efficiently. This contribution is particularly relevant to applications in autonomous vehicles, warehouse robotics, and service robots. While Xu's citation profile is still developing, their focus on combining perception, planning, and learning-based control positions them as an emerging voice in the intelligent robotics field, with work that bridges theoretical machine learning and practical robotic systems.
Research Focus
Key Achievements
Top Papers
- 1