Fujian Yan
Papers
9
Total Citations
44
H-Index
4
About
Fujian Yan’s research lies at the intersection of robotics, artificial intelligence, and human-robot collaboration, with a focus on enabling machines to understand and interact with complex human environments. His core contributions center on robotic scene understanding, spatial reasoning, and intuitive human-robot communication. Yan pioneered the integration of neural-logic learning with spatial relationship comprehension, allowing robots to infer object arrangements from minimal visual data—a critical advancement for autonomous grasping and assembly tasks. His work on controlled robot language and dictionary-based semantic analysis has further bridged the gap between human instruction and robotic action, enhancing reliability in collaborative settings. With over 40 citations across his most influential papers, Yan’s 2020 study on “Robotic Understanding of Spatial Relationships Using Neural-Logic Learning” (15 citations) stands as his most impactful, demonstrating a novel framework that combines deep learning with logical reasoning. His recent innovations, including touch detection on augmented surfaces and the “Omnisurface” concept (2025), push toward seamless human-robot teaming in shared workspaces. Yan’s research not only advances fundamental robotic perception but also directly informs practical applications in industrial assembly and collaborative manufacturing.
Research Focus
Key Achievements
Top Papers
- 1Robotic Understanding of Spatial Relationships Using Neural-Logic Learning15 citations · 2020
- 2
- 3Robotic Understanding of Object Semantics by Referringto a Dictionary7 citations · 2020
- 4Robotic Scene Understanding by Using a Dictionary5 citations · 2019
- 5Robotic Understanding of Scene Contents and Spatial Constraints3 citations · 2018
- 6
- 7Touch detection in augmented Omni-surface for human-robot teaming2 citations · 2022
- 8
- 9Omnisurface: Common Reality for Intuitive Human-Robot Collaboration1 citations · 2025