Liyue Xia

Papers

Total Citations

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About

Liyue Xia has made significant contributions to the field of robotic manipulation, with a primary focus on enhancing the autonomy and efficiency of arm robots in real-world applications. Her research centers on two key areas: adaptive grasping for irregular objects and distributed reward algorithms for inverse kinematics. In her most-cited work, "Robotic Grasping Position of Irregular Object Based Yolo Algorithm" (15 citations), Xia addresses the critical challenge of automatic grasping by integrating a modified YOLO object detection algorithm to identify optimal grasp points, thereby reducing failure rates in unstructured environments. Her second highly cited paper, "A Distributed Reward Algorithm for Inverse Kinematics of Arm Robot" (11 citations), introduces a novel reinforcement learning approach that replaces traditional analytical and numerical methods, enabling faster and more adaptive motion planning for complex robotic structures. These contributions are particularly notable for their practical impact, offering scalable solutions that minimize manual calibration and computational overhead. Xia’s work bridges computer vision and robotics, demonstrating how deep learning can streamline robotic control in dynamic settings. Her achievements underscore a commitment to advancing intelligent automation, making her research essential for students and engineers developing next-generation robotic systems.