Xuting Yang

Papers

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About

Xuting Yang is a leading researcher in intelligent robotic assembly, with a focus on integrating visual perception, force sensing, and reinforcement learning to solve complex manufacturing challenges. Their work has been instrumental in enabling robots to perform high-precision tasks, particularly the assembly of weak-stiffness parts that are prone to deformation. Yang’s most cited paper, "Skill learning for robotic assembly based on visual perspectives and force sensing" (2020, 29 citations), pioneered a framework that combines compliance control with deep learning, allowing robots to adapt to dynamic contact changes during assembly. Expanding on this, their 2022 study on Deep Deterministic Policy Gradient with a fuzzy logic reward function (11 citations) introduced a novel approach to peg-in-hole tasks, significantly improving adaptability in real-world scenarios. Yang also advanced force perception accuracy with a multi-parameter coupled model (2019, 8 citations), enhancing sensor calibration for flexible operations. With a growing citation record, Yang’s contributions are shaping the future of automated assembly, bridging the gap between simulation and practical industrial applications. Their work is essential reading for researchers in robotics, manufacturing, and AI-driven automation.