Yishi Han
Papers
1
Total Citations
10
H-Index
1
About
Yishi Han is a robotics researcher specializing in computer vision and robotic manipulation, with a particular focus on solving perception challenges for transparent and reflective objects. Her most cited work, "ClueDepth Grasp: Leveraging positional clues of depth for completing depth of transparent objects" (2022, 10 citations), addresses a critical bottleneck in robot grasping: the inability of standard RGB-D cameras to accurately perceive transparent objects due to refraction and reflection. By introducing a novel method that uses positional depth clues to complete missing depth information, Han enables humanoid robots to reliably grasp everyday transparent items—a task long considered difficult in robotics. This contribution bridges the gap between raw sensor data and practical manipulation, advancing the field of autonomous robotic interaction with complex, real-world objects. Han’s research is pivotal for applications in domestic robotics, manufacturing, and assistive technologies, where transparent objects are ubiquitous. Her work demonstrates a keen ability to identify and solve practical perception problems, making her a rising figure in robotic grasping and depth estimation.
Research Focus
Key Achievements
Top Papers
- 1