Tian Tan
Papers
1
Total Citations
8
H-Index
1
About
Tian Tan’s research lies at the intersection of robotic manipulation, computer vision, and grasp quality assessment. In their most-cited work, “Formulation and Validation of an Intuitive Quality Measure for Antipodal Grasp Pose Evaluation” (2021, 8 citations), Tan introduced a novel, intuitive grasp quality measure designed for real-time antipodal grasp evaluation. By analyzing object movement features derived from the interaction between a gripper and an object’s image-space projections, this method enables robots to assess grasp stability without complex physical models. This contribution is particularly valuable for applications in automated picking, assembly, and service robotics, where quick and reliable grasp decisions are critical. Tan’s approach bridges the gap between theoretical grasp metrics and practical, vision-based implementations, offering a computationally efficient solution that has been validated through rigorous experiments. While their citation count is still growing, this work represents a meaningful step toward more adaptive and perceptive robotic systems. Tan’s focus on intuitive, real-time evaluation methods positions them as a promising contributor to the field of robotic manipulation, with potential for significant impact on industrial and assistive robotics.
Research Focus
Key Achievements
Top Papers
- 1