Papers
2
Total Citations
13
H-Index
2
About
Haoran Lin is at the forefront of advancing robotic dexterous manipulation, with a specific focus on enabling robots to use tools with human-like precision. His research centers on two key areas: task-oriented tool manipulation and affordance learning from human-object interaction. Lin’s major contribution lies in bridging the gap between robotic grasping and functional tool use. In his 2024 work, he introduced a novel knowledge graph approach that maps specific functions to individual robotic fingers, moving beyond whole-hand grasping to achieve fine-grained control. This paper has already garnered 8 citations, signaling its impact on the field. Building on this, his 2025 study on granularity-aware affordances teaches robots to identify and interact with precise functional areas of tools, using affordance features as a critical bridge between object properties and task execution. With 5 citations in a short time, this work further solidifies his reputation. Lin’s research is not only technically innovative but also highly practical, directly addressing the challenge of enabling robots to perform complex, human-like tasks. His work is essential reading for anyone interested in the future of robotic manipulation and embodied AI.
Research Focus
Key Achievements
Top Papers
- 1
- 2