Yi-geng Dou
Papers
1
Total Citations
4
H-Index
1
About
Yi-geng Dou is a researcher advancing the frontier of human-robot collaboration, with a primary focus on reinforcement learning and intelligent control systems for robotic manipulation. His most cited work, "Human-Robot Interaction System Design for Manipulator Control Using Reinforcement Learning" (2021, 4 citations), introduces a novel dual-component HRI framework that seamlessly integrates an impedance model controller with a robotic arm controller. This design enables operators to intuitively and safely coordinate robotic arm operations, bridging the gap between human intent and machine precision. Dou’s contributions are particularly impactful in industrial automation and assistive robotics, where adaptive, real-time control is critical. By leveraging reinforcement learning, his system allows robots to learn and refine their responses during interaction, enhancing both efficiency and safety. Though early in his career, Dou’s work lays a strong foundation for more intuitive, responsive human-robot teams, promising to reshape how we deploy robots in complex, dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1