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About

Chunlei Li is a robotics researcher whose work centers on the integration of machine vision and intelligent control for industrial automation, with a particular focus on grinding and grasping applications. His major contributions lie in developing vision-guided robotic systems that enhance precision and flexibility in manufacturing processes, such as the automated edge grinding of metal surfaces and wheel hub castings. Li has pioneered a hybrid reinforcement learning approach that combines simulated annealing with proximal policy optimization (PPO) to improve robotic arm trajectory planning, addressing key challenges like local optimum traps and convergence difficulties. His research on robotic grasping integrates large kernel convolutions and residual connections to enable real-time, lightweight performance in complex environments. With over 15 citations across his most-cited papers, Li’s work demonstrates practical impact in bridging simulation and real-robot deployment. Notably, his 2025 study on vision-guided grinding for wheel hub castings highlights the growing role of “visual perception” in industrial robotics, positioning him as a contributor to the next generation of adaptive, perception-driven automation systems.