Xianling Deng
Papers
2
Total Citations
5
H-Index
1
About
Xianling Deng is a researcher in robotics and intelligent control systems, with a focus on robot learning from demonstration and autonomous manipulation. Their work bridges machine learning algorithms with robotic control, particularly in dynamic environments. Deng’s most cited paper, “A robot demonstration method based on LWR and Q-learning algorithm” (2018, 4 citations), introduces a novel approach that combines locally weighted regression with Q-learning to enable a 6-DOF robot to learn hitting tasks from human demonstrations and generate adaptive actions. This method exemplifies their contribution to making robots more flexible and capable of generalizing learned behaviors to new scenarios. In a related study, “A Robot Self-learning Grasping Control Method Based on Gaussian Process and Bayesian Algorithm” (2018, 1 citation), Deng explores probabilistic models for improving robotic grasping precision. Though early in their career, Deng’s work demonstrates a commitment to advancing robot autonomy through data-efficient learning techniques, offering practical pathways for robots to acquire skills with minimal human intervention. Their research holds promise for applications in manufacturing, service robotics, and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1A robot demonstration method based on LWR and Q-learning algorithm4 citations · 2018
- 2