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About

Jilai Song is a robotics and intelligent systems researcher whose work spans human-robot interaction, computer vision, and autonomous navigation. His research addresses some of the most pressing challenges in modern robotics: enabling machines to perceive, understand, and safely collaborate with humans in real-world environments. Song's most impactful contributions center on deep learning-based perception systems. His highly cited work on applying YOLO-based object detection to weld surface defect inspection (30 citations) and robotic grasping (10 citations) demonstrates a consistent effort to bridge industrial automation with state-of-the-art neural network architectures. His 2022 paper on real-time collision avoidance for safe human-robot interaction (34 citations) stands as his most recognized achievement, reflecting growing demand for reliable collaborative robotics in manufacturing settings. Beyond perception, Song has made meaningful contributions to simultaneous localization and mapping (SLAM), proposing a 3D LiDAR-based approach enhanced with ground segmentation and loop detection, as well as GPU-accelerated ORB-SLAM2 implementations. His earlier work on human motion prediction frameworks and quadrupedal robot dynamics further illustrates the breadth of his expertise. Collectively, Song's research supports the advancement of intelligent, safe, and efficient robotic systems for both industrial and autonomous applications.