Lei Lv
Papers
1
Total Citations
13
H-Index
1
About
Lei Lv is a researcher advancing intelligent control systems for humanoid robotics, with a primary focus on integrating deep reinforcement learning with stability-constrained motion planning. His most-cited work, "Deep reinforcement learning-based attitude motion control for humanoid robots with stability constraints" (2020, 13 citations), addresses critical challenges in achieving precise motion-manipulation control and maintaining dynamic balance—problems that have long hindered humanoid robot deployment in real-world environments. By tackling the dual issues of limited physical training samples and low learning efficiency, Lv developed algorithms that enable robots to learn robust attitude control policies under stability constraints, bridging the gap between simulation and physical execution. His contributions are particularly notable for their practical implications in human-robot interaction and autonomous navigation, where reliable balance is paramount. Though his citation count reflects a focused, emerging impact, Lv’s work represents a meaningful step toward more agile and adaptive humanoid systems, offering a foundation for future research in reinforcement learning-based control for complex robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1