Jianjun Chen
Papers
1
Total Citations
12
H-Index
1
About
Dr. Jianjun Chen is a leading researcher in robotics and autonomous navigation, with a focus on real-time, model-based learning for complex environments. His most notable contribution is the development of NeuPAN (Neural Proximal Alternating-minimization Network), introduced in his highly cited 2025 paper (12 citations), which revolutionizes nonholonomic robot navigation by integrating end-to-end model-based learning with direct point-based perception. This work enables robots to navigate cluttered, unknown spaces without pre-built maps, achieving exceptional accuracy and real-time collision avoidance—a breakthrough for deployable autonomous systems. Dr. Chen’s research bridges the gap between perception and motion control, offering a scalable, easy-to-deploy solution that has garnered significant attention in the robotics community. His achievements underscore a commitment to advancing practical, map-free navigation, making his work essential for students and researchers exploring autonomous robotics, deep learning in control systems, and real-time decision-making under uncertainty.
Research Focus
Key Achievements
Top Papers
- 1NeuPAN: Direct Point Robot Navigation With End-to-End Model-Based Learning12 citations · 2025