Seong Dae Kim
Papers
1
Total Citations
24
H-Index
1
About
Seong Dae Kim is a leading researcher in the intersection of collaborative robotics, digital twin technology, and human-robot interaction. His work focuses on developing intuitive control frameworks that bridge the gap between human operators and robotic systems, particularly through imitation learning and teleoperation. Kim’s most cited paper, "Control framework for collaborative robot using imitation learning-based teleoperation from human digital twin to robot digital twin" (2022, 24 citations), introduces a novel paradigm where a human digital twin—a virtual replica of the operator—enables seamless, skill-transferable control of a robot digital twin. This approach not only enhances the safety and efficiency of collaborative robots in manufacturing and healthcare but also reduces the cognitive load on human operators by leveraging learned behaviors. His contributions are pivotal in advancing human-robot collaboration, offering scalable solutions for complex tasks. With a growing citation impact, Kim’s work is recognized for its practical applications in Industry 4.0, and he continues to shape the future of teleoperation and autonomous systems through innovative digital twin integration.
Research Focus
Key Achievements
Top Papers
- 1