Sejun Park
Papers
2
Total Citations
7
H-Index
2
About
Sejun Park’s research lies at the intersection of robotics, imitation learning, and physics-informed machine learning, with a focus on enabling robots to perform complex, real-world tasks with precision and autonomy. In his most-cited work, Park proposed a direct demonstration-based imitation learning method for robot manipulators, integrating an impedance controller to track both desired position and force. This approach allows robots to replicate human-demonstrated actions—such as writing—even when starting from different initial positions, significantly advancing the practicality of learning from demonstration. His follow-up work introduces a state observer-based physics-informed machine learning framework for leader-following tracking control of mobile robots, blending physical laws with data-driven models for robust, adaptive control. Though early in his career, with papers accumulating 4 and 3 citations respectively, Park’s contributions are foundational for developing robots that learn intuitively and operate safely in unstructured environments. His work is particularly notable for bridging model-based control and data-driven learning, offering a pathway toward more intelligent, human-compatible robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2