Haozhuo Li
Papers
1
Total Citations
4
H-Index
1
About
Haozhuo Li is a robotics researcher whose work centers on imitation learning, human-robot interaction, and the design of intuitive interfaces for robotic skill acquisition. His most cited paper, "How to Train Your Robots? The Impact of Demonstration Modality on Imitation Learning" (2025, 4 citations), makes a foundational contribution by systematically investigating how different demonstration modalities—such as kinesthetic teaching, teleoperation, and video demonstrations—affect the quality and efficiency of robot learning from human data. Li’s research reveals that modality choice significantly influences data diversity, task success rates, and user effort, providing critical guidelines for designing more effective and accessible robot training systems. His work bridges the gap between human demonstration and machine learning, offering practical insights for both roboticists and end-users. By highlighting the trade-offs between ease of use and data richness, Li’s findings help shape future interfaces for non-expert robot programming. With a growing citation impact, Haozhuo Li is establishing himself as a key voice in making robot learning more robust, user-friendly, and scalable—an essential step toward deploying capable robots in homes and workplaces.
Research Focus
Key Achievements
Top Papers
- 1