Lei Lyu
Papers
2
Total Citations
7
H-Index
2
About
Lei Lyu is a researcher advancing intelligent robotics, with a focus on autonomous navigation and adaptive task planning for service robots. His work addresses critical challenges in enabling robots to operate effectively in dynamic, real-world environments. Lyu's notable contribution, "A Virtual End-to-End Learning System for Robot Navigation Based on Temporal Dependencies" (2020, 5 citations), tackles the complex task of steering wheeled mobile robots by converting front-facing camera data into steering angles using convolutional neural networks, overcoming limitations of existing methods. More recently, in "Efficiency-Driven Adaptive Task Planning for Household Robot Based on Hierarchical Item-Environment Cognition" (2025, 2 citations), he explores task planning for household robots, developing systems that allow robots to execute unfamiliar services through hierarchical cognition of items and environments. This work addresses a conventional yet complex domain with extensive applications in robotics. Lyu's research bridges deep learning and cognitive robotics, contributing to more capable and efficient autonomous systems. His focus on end-to-end learning and adaptive planning positions him at the forefront of developing practical, intelligent robots for everyday environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2