Ruiquan Ge
Papers
1
Total Citations
5
H-Index
1
About
Dr. Ruiquan Ge is a researcher whose work lies at the intersection of robotics, computer vision, and deep learning, with a particular focus on autonomous navigation. His most-cited contribution, "A Virtual End-to-End Learning System for Robot Navigation Based on Temporal Dependencies" (2020), tackles the complex challenge of steering wheeled mobile robots through diverse environments. In this work, Dr. Ge addresses a critical limitation of conventional convolutional neural network (CNN) approaches: their inability to effectively leverage temporal information from sequential camera data. By proposing a virtual end-to-end learning system that incorporates temporal dependencies, he offers a more robust framework for converting front-facing video streams into accurate steering commands. While his citation count is still growing—reflecting the early stage of this impactful research direction—this work has already garnered 5 citations, signaling its relevance to the autonomous navigation community. Dr. Ge’s contributions are particularly valuable for students and researchers exploring how temporal dynamics can enhance robotic perception and control, bridging the gap between simulated training environments and real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1