Zehan Tan
Papers
1
Total Citations
2
H-Index
1
About
Zehan Tan is a researcher focused on advancing robotic perception and manipulation, particularly in the domain of deformable object segmentation for autonomous systems. Their key research areas include computer vision, robotics, and dataset creation for indoor environments. Tan’s major contribution is the introduction of the DOS Dataset (Deformable Object Segmentation Dataset), a novel resource designed to improve the ability of sweeping robots to recognize and interact with non-rigid objects like cables, fabrics, and sponges. This work addresses a critical gap in robotic navigation and cleaning tasks, where traditional rigid-object detection fails. The dataset, presented in 2023, has already garnered early citations, signaling its growing importance in the field. Tan’s research is notable for its practical application—enhancing the autonomy and safety of household robots—and for providing a benchmark that enables further studies in deformable object segmentation. By tackling a challenging, underexplored problem, Tan is contributing to the next generation of intelligent, adaptive robots capable of operating in complex, unstructured environments. Their work holds promise for both academic research and real-world deployment in smart home technologies.
Research Focus
Key Achievements
Top Papers
- 1