Xiao Meng

About

Xiao Meng is a leading researcher in robotic precision manufacturing, specializing in intelligent control systems for industrial grinding and surface tracking. His work addresses a critical challenge in automated manufacturing: achieving consistent, high-quality material removal through adaptive force control. Meng’s most influential contribution is the development of a press-and-release model combined with model-based reinforcement learning for robotic constant-force grinding (2019, 39 citations), which significantly improves stability during the impact and processing stages of grinding. He further advanced the field by integrating neural networks for angle identification with reinforcement learning-based force control, enabling robots to accurately track curved surfaces (2020, 29 citations). His iterative algorithm approach (2020) provides a practical framework for real-time force adjustment, reducing surface defects and tool wear. With over 73 cumulative citations on these core papers, Meng’s work bridges theoretical control theory and practical industrial automation, offering robust solutions for aerospace, automotive, and precision manufacturing sectors. His research has been instrumental in moving robotic grinding from laboratory settings to reliable, high-precision industrial applications.

Research Focus

Key Achievements

Top Papers

1. 1
   Robotic constant-force grinding control with a press-and-release model and model-based reinforcement learning

   39 citations · 2019
2. 2
   Robotic Curved Surface Tracking with a Neural Network for Angle Identification and Constant Force Control based on Reinforcement Learning

   29 citations · 2020
3. 3
   Robotic constant force grinding control based on grinding model and iterative algorithm

   5 citations · 2020

Key Collaborators

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