Jing Huo
Papers
2
Total Citations
7
H-Index
2
About
Jing Huo is a leading researcher in the field of robotic manipulation, with a primary focus on enabling robots to master complex, long-horizon tasks through advanced imitation learning and skill chaining. Her work addresses the critical challenge of breaking down intricate, multi-stage operations into learnable, executable sequences. Huo’s major contributions include the development of novel frameworks that refine how robots acquire and replicate expert behaviors. Her paper "SCaR: Refining Skill Chaining for Long-Horizon Robotic Manipulation via Dual Regularization" introduces a dual regularization technique to stabilize skill transitions, while "Cognizing and Imitating Robotic Skills via a Dual Cognition-Action Architecture" proposes a hierarchical model that separates cognitive understanding from motor execution, significantly improving imitation fidelity. Though her most-cited papers are recent (2024), they are already garnering attention, with SCaR accumulating 5 citations. This early impact signals her work’s relevance to the growing demand for more autonomous and capable robotic systems. Huo’s research is paving the way for robots that can learn and perform complex, real-world tasks with greater reliability and efficiency.
Research Focus
Key Achievements
Top Papers
- 1
- 2