Anzhe Chen
Papers
1
Total Citations
2
H-Index
1
About
Anzhe Chen is a robotics researcher whose work focuses on advancing visual servoing—a critical technique for enabling robots to perceive and interact with their environments. Chen’s key contributions lie in developing adaptive, calibration-free approaches that reduce the labor-intensive process of camera calibration for industrial robots. In their most-cited paper, "Adapting for Calibration Disturbances: A Neural Uncalibrated Visual Servoing Policy" (2024), Chen proposes a neural policy that allows robots to maintain precise control even when camera parameters are inaccurate or change over time. This innovation addresses a major bottleneck in deploying hundreds of robots in factories, where individual calibration is impractical. By integrating learning-based methods with traditional control, Chen’s work enhances robustness and scalability in real-world automation. With 2 citations to date, this early-career contribution signals growing interest in their approach. Chen’s research bridges the gap between theoretical control systems and practical deployment, making visual servoing more accessible for large-scale industrial applications. Their work is particularly valuable for students and researchers exploring neural control policies, calibration-free robotics, and adaptive systems in manufacturing.
Research Focus
Key Achievements
Top Papers
- 1