Shengmei Shen
Papers
1
Total Citations
72
H-Index
1
About
Shengmei Shen is a researcher specializing in autonomous navigation, robotics, and deep learning, with a focus on developing intelligent systems capable of goal-directed movement in complex, real-world environments. Their most notable contribution is the development of **Intention-Net**, a pioneering two-level hierarchical navigation framework introduced in 2017 that elegantly bridges model-free deep learning with model-based path planning. This innovative approach addresses one of robotics' most practical challenges: enabling delivery robots to navigate reliably through unfamiliar environments, such as new office buildings, with minimal prior information. By integrating the adaptability of neural networks at the low level with the structured reasoning of classical planning at a higher level, Shen's work offers a compelling solution to robust autonomous navigation under real-world uncertainty. The paper has garnered 72 citations, reflecting its meaningful influence within the robotics and AI research communities. Shen's research sits at an exciting intersection of perception, planning, and machine learning, making their work particularly relevant for students and practitioners interested in how intelligent systems can operate autonomously and purposefully in dynamic, previously unseen environments.
Research Focus
Key Achievements
Top Papers
- 1