Xiaofei Fu

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About

Xiaofei Fu is a leading researcher in intelligent inspection and cognitive robotics for power systems, with a focus on bridging the gap between perceptual and cognitive intelligence. Their most-cited work, "Object recognition for power equipment via human‐level concept learning" (2021, 13 citations), pioneers a novel approach that enables inspection robots to move beyond simple pattern recognition toward human-like concept learning, allowing them to automatically detect defects in power equipment with greater adaptability. This contribution addresses a critical bottleneck in substation automation, where traditional robots lack the cognitive capacity to handle novel or ambiguous scenarios. Fu’s research integrates machine learning, computer vision, and robotics to enhance the reliability and autonomy of power infrastructure monitoring. By advancing human-level concept learning for industrial applications, Fu has laid the groundwork for more intelligent, self-improving inspection systems. Their work is particularly notable for its practical impact on reducing manual inspection burdens and improving operational safety in energy sectors. With growing citation influence, Xiaofei Fu continues to shape the future of cognitive robotics in critical infrastructure.