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About

Fangming Liu is a dynamic researcher at the forefront of robotics, edge computing, and intelligent navigation systems. His work centers on two interconnected domains: autonomous navigation for air-ground robots (AGRs) and efficient deep learning inference for robotic IoT applications. Liu's contributions have meaningfully advanced how robots perceive and navigate complex, real-world environments, particularly in high-stakes scenarios such as disaster response and surveillance operations. Among his most notable achievements, Liu developed OMEGA, an occlusion-aware navigation framework leveraging state space models to enable AGRs to operate safely in dynamic, obstacle-rich environments — a problem that had long challenged existing systems. Complementing this, his HE-Nav system pushed the boundaries of high-performance navigation in cluttered spaces, addressing critical limitations in voxel occupancy prediction and path planning. His parallel research into distributed DNN inference tackles the pressing challenge of deploying computationally intensive machine learning models on resource-constrained robotic platforms efficiently. With multiple papers already accumulating citations in 2024 alone — including works garnering up to 8 citations within months of publication — Liu's research is rapidly gaining traction. His interdisciplinary approach, bridging robotics, machine learning, and systems engineering, positions him as an emerging and impactful voice in autonomous systems research.