Yuandong Hu
Papers
1
Total Citations
5
H-Index
1
About
Yuandong Hu is a robotics researcher whose work centers on dexterous manipulation, human–robot skill transfer, and learning from demonstration. His most-cited paper, "Learn to Grasp Objects with Dexterous Robot Manipulator from Human Demonstration" (2022, 5 citations), introduces a method that uses dynamic motion primitives (DMPs) to model and transfer human grasping trajectories to multi-fingered robotic hands. By having users physically guide the robot through a task, Hu captures natural 3D joint motions and encodes them into reusable movement primitives, enabling the robot to replicate and generalize complex grasps. This contribution addresses a core challenge in robotics: bridging the gap between human dexterity and robotic control. Hu’s work is notable for its practical, demonstration-driven approach, which reduces the need for explicit programming and makes advanced manipulation more accessible. While his citation count is still growing, his research lays important groundwork for intuitive human–robot collaboration, with potential applications in assistive robotics, manufacturing, and prosthetics. His focus on skill transfer continues to influence how robots learn from human expertise.
Research Focus
Key Achievements
Top Papers
- 1