Mingsheng Yin

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About

Mingsheng Yin is a pioneering researcher at the intersection of wireless communications and autonomous robotics, specializing in millimeter wave (mmWave) navigation and physics-informed machine learning. His work addresses the critical challenge of enabling robots to navigate indoor environments using high-frequency wireless signals, achieving precise localization even in cluttered or partially observed spaces. Yin’s most influential paper, "Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification" (2022, 20 citations), demonstrates how mmWave’s high angular and temporal resolution can be harnessed for target localization, establishing a foundational approach for wireless-aided robotics. He further advanced the field with "Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning" (2024, 7 citations), a breakthrough that integrates radio frequency propagation principles with reinforcement learning, enabling robots to generalize navigation strategies across unseen environments without retraining. His work on "Path Planning Under Uncertainty to Localize mmWave Sources" (2023) introduces novel estimation and planning algorithms using Extended Kalman Filters, while "Wireless Channel Prediction in Partially Observed Environments" (2022) pioneers methods to extract statistical channel models from visual data like cameras and LIDAR. With a growing citation impact and a focus on real-world deployment, Yin is shaping the future of autonomous navigation in dynamic, wireless-rich environments.