Yinchuan Li
Papers
1
Total Citations
4
H-Index
1
About
Yinchuan Li is a robotics researcher whose work focuses on advancing imitation learning for complex bimanual manipulation. His primary research areas include spatial-temporal modeling, graph neural networks, and kinematic policy learning for dual-arm robotic systems. Li's major contribution lies in developing the Spatial-Temporal Graph Diffusion Policy, which integrates kinematic modeling with diffusion-based policy learning to address the fundamental challenges of coordinating two robotic arms simultaneously. His work introduces a novel framework that predicts distant next-best end-effector poses and efficiently computes corresponding joint rotations, significantly improving dexterous manipulation capabilities. While his most-cited paper from 2025 has garnered 4 citations—a strong start for recent work—Li's research represents an important step toward more natural and efficient bimanual robotic control. His approach stands out for combining graph-based spatial-temporal reasoning with diffusion models, offering a promising solution to one of robotics' most persistent challenges: enabling robots to perform coordinated two-handed tasks with the fluidity and precision previously reserved for human operators.
Research Focus
Key Achievements
Top Papers
- 1