Jiaxing Liang
Papers
2
Total Citations
9
H-Index
2
About
Jiaxing Liang is a pioneering researcher in intelligent robotic systems, with a primary focus on vision-guided automation and reinforcement learning for industrial manipulation. His work centers on two transformative areas: integrating computer vision with robotic grinding for precision manufacturing, and advancing trajectory planning through hybrid optimization algorithms. Liang’s 2025 study on vision-guided grinding robots, which has already garnered 5 citations, demonstrates how equipping robots with “visual perception” enables accurate localization and efficient execution of complex grinding tasks for wheel hub castings—a breakthrough that directly addresses real-world industrial challenges. In parallel, his innovative approach to robotic arm grasping, earning 4 citations, introduces a novel hybrid reinforcement learning framework that combines simulated annealing with proximal policy optimization (PPO). This work overcomes persistent obstacles in unstructured environments, such as local optimum traps and limited real-time interaction, while enabling successful real-robot migration. Liang’s contributions are particularly notable for bridging the gap between theoretical optimization and practical deployment, making his research highly relevant for students and engineers working at the intersection of robotics, computer vision, and adaptive control systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2