Papers
1
Total Citations
19
H-Index
1
About
Liwei Huang is a researcher whose work sits at the intersection of robotics, artificial intelligence, and autonomous navigation. His primary focus is on developing intelligent control systems that enable mobile robots to operate safely and efficiently in complex, changing environments. Huang’s most cited paper, “Reinforcement Learning for Mobile Robot Obstacle Avoidance Under Dynamic Environments” (2018), with 19 citations, introduces a novel approach that leverages reinforcement learning to help robots adapt their path-planning in real time, moving beyond static obstacle avoidance to handle unpredictable, moving obstacles. This work has been influential in advancing practical, learning-based solutions for autonomous navigation, a critical challenge in fields from warehouse logistics to assistive robotics. By demonstrating that robots can learn robust avoidance strategies through trial and error, Huang has contributed to a growing body of research that bridges theoretical reinforcement learning with real-world robotic applications. His contributions are particularly valuable for students and engineers seeking to deploy adaptive, safe, and intelligent robots in dynamic settings, making his research a key reference in the ongoing evolution of autonomous systems.
Research Focus
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Top Papers
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