Xuanzhi Wang

Nanjing University of Posts and Telecommunications, Peking University

Papers

Total Citations

H-Index

About

Xuanzhi Wang is a rising researcher in robotics and autonomous systems, with a focus on intelligent navigation, exploration, and sensing in unknown or complex environments. His work bridges deep reinforcement learning and robotic autonomy, most notably in his 2023 paper on "Deep reinforcement learning-aided autonomous navigation with landmark generators" (15 citations), which addresses a critical challenge in mobile robot accuracy by integrating learned landmark generation into navigation pipelines. Wang also co-authored "Efficient Autonomous Exploration and Mapping in Unknown Environments" (8 citations), introducing methods that account for regional legacy issues—the outsized impact of small unexplored areas on overall exploration efficiency. In the sensing domain, his work "LoCal" (8 citations) tackles the practical problem of transforming millimeter wave radar estimates from radar to room coordinates, a key step for enabling reliable tracking in diverse applications. Together, these contributions demonstrate Wang’s ability to identify and solve fundamental bottlenecks in autonomous systems, from high-level planning to low-level sensor calibration. With over 30 citations across his early-career publications, Wang is establishing himself as a thoughtful innovator in the intersection of learning, perception, and robotic autonomy.