Linghan Zhong
Papers
1
Total Citations
5
H-Index
1
About
Linghan Zhong is a robotics researcher whose work focuses on enabling efficient robot learning through policy transfer across diverse environments. Their key research areas include domain adaptation, sim-to-real transfer, and grounding techniques for robotic systems. Zhong’s major contribution lies in developing methods to bridge visual and dynamics domain gaps, allowing policies trained in one environment—such as a simulator or laboratory—to be effectively deployed in another, without task-specific supervision. Their most cited paper, "Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding" (2021), introduces an iterative grounding framework that aligns disparate domains, achieving robust transfer with only 5 citations to date but representing a foundational step in scalable robot learning. This work addresses a critical challenge in robotics: reducing the need for costly real-world data collection by leveraging accessible training environments. Zhong’s research has implications for autonomous systems, manipulation, and field robotics, where adaptability across varied settings is essential. Their achievements highlight a commitment to practical, generalizable solutions that push the boundaries of how robots learn and operate in the real world.
Research Focus
Key Achievements
Top Papers
- 1