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About

Shibo Han is a leading researcher in robotic machining and intelligent control, whose work bridges the gap between autonomous systems and complex physical interactions. His primary research areas include mobile robot machining, impedance control, and simultaneous localization and mapping (SLAM) for industrial applications. Han’s most impactful contribution is his 2021 paper on accuracy analysis in mobile robot machining of large-scale workpieces, which has garnered 65 citations and addresses critical challenges in precision manufacturing. He further advanced the field with his model-based actor–critic learning algorithm for robotic impedance control, a 45-citation study that enables robots to safely learn optimal interaction skills in unknown environments—a breakthrough for human–robot collaboration and machining tasks. Han has also explored monocular SLAM for indoor navigation and developed automatic programming methods for dual-robot grinding of intersecting curves, demonstrating his versatility in both perception and manipulation. His work is distinguished by its practical focus on safety and adaptability, making him a key figure in the evolution of intelligent robotic systems for real-world manufacturing and service applications.