Jianhe Yuan
Papers
1
Total Citations
30
H-Index
1
About
Jianhe Yuan is a researcher at the forefront of computer vision and robotics, specializing in category-level object pose estimation, affordance prediction, and 3D reconstruction. His most notable contribution is the HANDAL dataset, a pioneering resource focused on real-world, manipulable objects—such as pliers, utensils, and screwdrivers—that are sized and shaped for functional robotic grasping. Unlike prior datasets, HANDAL provides rich annotations including pose, affordances, and reconstructions, bridging a critical gap between vision and manipulation. Since its release in 2023, the dataset has garnered 30 citations, reflecting its immediate impact on the robotics and vision communities. Yuan’s work is instrumental in enabling robots to perceive and interact with everyday objects more intelligently, advancing the field toward practical, real-world applications. His research stands out for its emphasis on robotics-ready benchmarks, making him a key contributor to the development of more capable and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1