Jianhang Yan
Papers
1
Total Citations
23
H-Index
1
About
Jianhang Yan is a researcher advancing the field of robotic manipulation, with a primary focus on grasp pose estimation and transfer learning for robotic grasping. His key contributions center on developing methods that enable robots to grasp objects from a category by learning from just a single labeled instance, dramatically reducing the need for extensive annotated datasets. His most-cited work, "TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance" (2022), has garnered 23 citations, reflecting its impact on efficient, generalizable robotic grasping. This approach addresses a critical bottleneck in robotics: the ability to adapt grasping strategies across varied object shapes and appearances without retraining from scratch. Yan’s research is notable for its practical implications in automation and service robotics, where quick adaptation to new objects is essential. His work stands out for its elegant combination of category-level reasoning and transfer learning, offering a scalable path toward more versatile robotic systems. For students and researchers, Yan’s contributions exemplify how targeted innovations in learning algorithms can bridge the gap between lab demonstrations and real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1