Sungyong Park
Papers
1
Total Citations
21
H-Index
1
About
Sungyong Park is a leading researcher in robotics, with a primary focus on robot manipulation, cooperative systems, and skill generalization. His most impactful work centers on developing algorithms that enable robots to learn and adapt complex, multi-step tasks from human demonstrations. Park’s seminal paper, "Learning and Generalizing Cooperative Manipulation Skills Using Parametric Dynamic Movement Primitives," has garnered 21 citations and introduces a powerful framework for generating the full trajectories of mobile manipulators during intricate missions. By leveraging Parametric Dynamic Movement Primitives (PDMPs), his approach allows robots to quickly generalize learned motions online, moving beyond simple repetition to robustly handle novel scenarios. This work is critical for advancing human-robot collaboration, where robots must fluidly adapt to changing environments and tasks. Park’s contributions are shaping the future of autonomous systems, making robots more versatile and intuitive partners in manufacturing, service, and beyond.
Research Focus
Key Achievements
Top Papers
- 1