Xianwu Xie

Wuhan University of Science and Technology

Papers

Total Citations

H-Index

About

Xianwu Xie is a robotics researcher whose work centers on intelligent control, robot learning, and autonomous manipulation. His key contributions lie at the intersection of machine learning and robotic grasping, where he has developed methods that enable robots to adapt to dynamic environments. Xie’s most-cited paper, “An RBF-PD Control Method for Robot Grasping of Moving Object” (2018, 4 citations), introduces a radial basis function-based control strategy for real-time grasping of moving targets. In another influential work, “A robot demonstration method based on LWR and Q-learning algorithm” (2018, 4 citations), he proposed a novel approach combining locally weighted regression with reinforcement learning, allowing a 6-DOF robot to learn and generate new actions from demonstrations. This method demonstrates how robots can adapt to tasks without explicit programming. Xie’s research also explores Gaussian processes and Bayesian algorithms for self-learning grasping control. Though his citation counts are modest, his contributions are foundational in advancing adaptive, learning-based robotic systems, particularly for applications requiring real-time interaction with moving objects. His work exemplifies the growing trend toward autonomous, data-driven robot control.