Yujin Qi
Papers
1
Total Citations
2
H-Index
1
About
Yujin Qi is a leading researcher in robot manipulation and embodied AI, with a focus on enabling generalizable, multi-task assembly skills for autonomous systems. Their most cited work, "Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation" (2025, 2 citations), introduces a novel framework for teaching robots to perform complex 3D assembly tasks—such as furniture assembly and component fitting—by learning pairwise object interactions. This work addresses a critical gap in existing benchmarks, which primarily target geometric fragments or factory parts, and advances toward practical, real-world home robotics. Qi’s contributions lie in developing scalable, multi-task learning approaches that improve robot generalization across diverse assembly scenarios. Their research has significant implications for automating manual labor and enhancing human-robot collaboration. With a growing citation impact, Qi is recognized for pushing the boundaries of dexterous manipulation and task-level generalization, making their work essential reading for students and researchers in robotics, computer vision, and AI-driven automation.
Research Focus
Key Achievements
Top Papers
- 1