Xiaokang Wang

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About

Xiaokang Wang is a leading researcher at the intersection of industrial robotics and cyber-physical systems, with a primary focus on intelligent automation for remanufacturing and adaptive manufacturing. Wang’s major contributions lie in developing robust, data-driven solutions for two critical challenges: robotic grasping and disassembly planning. In the realm of grasping, Wang pioneered a region-based detection method for industrial cyber-physical systems, enabling robots to dynamically identify optimal grasp points for emergent tasks—a concept termed "Custom Grasping." This work, published in 2022, has already garnered 39 citations for its practical approach to reconfigurable robotics. Equally impactful is Wang’s work on robotic disassembly sequence planning, a cornerstone of sustainable remanufacturing. Recognizing that returned products often suffer from rust, deformation, or missing parts, Wang introduced backup actions into disassembly algorithms, allowing robots to adapt plans in real-time under uncertainty. This 2021 paper, with 28 citations, directly addresses a key bottleneck in the circular economy. Wang’s research is notable for bridging theoretical planning with real-world industrial uncertainty, making automation more resilient and efficient.