Yefan Zhou
Papers
1
Total Citations
9
H-Index
1
About
Yefan Zhou is a robotics researcher whose work focuses on data-efficient learning for robotic manipulation, particularly in the domain of grasping. His major contribution lies in addressing a critical bottleneck in deep learning-based grasping: the scarcity of labeled training data. In his highly cited 2022 paper, "Learn to Grasp with Less Supervision," Zhou introduced a novel maximum likelihood grasp sampling loss that enables models to learn effective grasping policies from sparsely labeled datasets. This approach significantly reduces the need for expensive, manually annotated grasp labels, making robotic grasping more practical and scalable for real-world applications. With 9 citations, this work has already influenced subsequent research in data-efficient robot learning. Zhou’s research sits at the intersection of computer vision, deep learning, and robotics, aiming to bridge the gap between simulation-trained models and real-world deployment. His work is particularly valuable for students and researchers interested in reducing the data burden in robotic learning, offering a path toward more autonomous and adaptable manipulation systems that can handle diverse objects with minimal supervision.
Research Focus
Key Achievements
Top Papers
- 1