Yaqi Hu

Papers

Total Citations

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About

Yaqi Hu is pioneering the integration of millimeter-wave (mmWave) wireless communications with autonomous robot navigation, addressing critical challenges in indoor localization and path planning. Their research focuses on leveraging the high angular and temporal resolution of mmWave signals to enable precise robot positioning in cluttered, GPS-denied environments. Hu’s most cited work, “Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification” (2022, 20 citations), introduces a novel framework that classifies wireless link states to enhance localization accuracy, demonstrating the potential of mmWave for real-world robotic applications. In “Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning” (2024, 7 citations), Hu advances the field by combining radio frequency propagation physics with reinforcement learning, enabling robots to navigate unseen environments without prior training—a breakthrough for generalizable, adaptive systems. Their paper “Path Planning Under Uncertainty to Localize mmWave Sources” (2023, 4 citations) further contributes by developing estimation and planning algorithms that handle uncertainty in dynamic indoor settings. Hu’s work on wireless channel prediction from partially observed visual data (2022, 2 citations) also highlights their interdisciplinary approach, merging computer vision and RF modeling. With a growing citation record and innovative methods that bridge theory and practice, Yaqi Hu is shaping the future of wireless-aware robotics, offering scalable solutions for smart factories, autonomous delivery, and search-and-rescue operations.