Yi-Lun Huang
Papers
1
Total Citations
5
H-Index
1
About
Yi-Lun Huang is a robotics researcher whose work lies at the intersection of computer vision and autonomous manipulation, with a particular focus on enabling home service robots to interact intelligently with their environments. His most cited work, "A 3D vision based object grasping posture learning system for home service robots" (2017, 5 citations), introduces a novel system that allows robots to recognize object orientation and select feasible grasping points by analyzing the surrounding environment. This contribution addresses a critical challenge in service robotics: enabling machines to adapt their grasping strategies in real-time when initial planned postures prove inadequate. Huang's approach integrates 3D vision with learning-based posture selection, moving beyond simple pre-programmed grasps toward more flexible, context-aware manipulation. While his citation count reflects the emerging nature of his research area, his work represents an important step toward practical, vision-guided robotic assistants capable of operating in unstructured home environments. For students and researchers in robotics and computer vision, Huang's research demonstrates how combining geometric reasoning with adaptive learning can bridge the gap between laboratory demonstrations and real-world deployment of service robots.
Research Focus
Key Achievements
Top Papers
- 1