Yu Lu Hwang
Papers
1
Total Citations
6
H-Index
1
About
Yu Lu Hwang is a researcher whose work sits at the intersection of robotics, computer vision, and intelligent control systems. Her primary research focus is on developing advanced control strategies for robotic manipulation, with a particular emphasis on visual servoing—the use of visual feedback to guide robot motion. Her most notable contribution, "Image base visual servoing based on reinforcement learning for robot arms" (2017), introduces a novel approach that leverages reinforcement learning to design an adaptive gain controller for robot arms. In this work, Hwang uses image feature errors to define the state space, enabling the robot to perceive its environment through a camera and adjust its actions accordingly. This innovative fusion of reinforcement learning with visual servoing has garnered significant attention, accumulating 6 citations and laying the groundwork for more intelligent, autonomous robotic systems. Hwang's work is particularly impactful for students and researchers interested in how robots can learn to interact with dynamic environments, offering a practical framework for integrating machine learning into real-world control applications.
Research Focus
Key Achievements
Top Papers
- 1Image base visual servoing base on reinforcement learning for robot arms6 citations · 2017