Xiaochun Ji — accepted AI-conference papers

2026

Assembly in Motion With a Mobile Manipulator Based on Servoing Control and Disturbance Compensation

RA-L 2026

Assembly in motion is a key solution to enhancing system efficiency in dynamic manufacturing environments. However, kinematic uncertainties and external disturbances often hinder mobile manipulators from performing high-precision tasks. This paper addresses this challenge by proposing a robust servo

Cited by 0 Source ↗Scholar ↗

2025

Task-Parameterized Dynamic Movement Primitives With Reinforcement Learning for Improved Motion Planning

Kaiqi Huang, Xiaochun Ji, Jianhua Su, Xiaoyi Qu

RA-L 2025

Online trajectory planning in unstructured environments poses significant challenges for mobile robots, particularly when navigating complex obstacles. Traditional learning-from-demonstration (LfD) methods depend on offline datasets, limiting their ability to adapt to varying obstacle shapes and dyn

Cited by 2 Source ↗Scholar ↗