Yawen Li

Beijing University of Posts and Telecommunications

Papers

Total Citations

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About

Yawen Li is a researcher at the forefront of intelligent robotics and autonomous navigation, with a particular focus on enabling safe, collision-free movement in highly unstructured and cluttered environments. Her work addresses a critical challenge in modern robotics: how to make deep reinforcement learning (DRL) systems not only adaptive but also data-efficient and robust in asymmetric, complex settings. Her most-cited paper, "Laser Based Navigation in Asymmetry and Complex Environment" (2022), has garnered 2 citations and lays the groundwork for a new generation of navigation algorithms that reduce the need for extensive human tuning. By tackling the inherent asymmetry in DRL—where data efficiency often lags behind adaptability—Li’s contributions help bridge the gap between theoretical reinforcement learning and practical, real-world deployment. Her research is particularly valuable for applications in search-and-rescue, autonomous exploration, and industrial automation, where environments are unpredictable and safety is paramount. As a rising voice in the field, Yawen Li’s work is shaping the future of resilient, self-learning robotic systems.