Papers
13
Total Citations
168
H-Index
6
About
Yanwei Fu is a leading researcher at the intersection of computer vision, natural language processing, and robotics, with a primary focus on enabling robots to perceive, understand, and manipulate objects in the physical world. His key research areas include category-level 6D object pose and size estimation, language-guided robotic grasping, and interactive manipulation. Fu’s major contributions include the development of SAR-Net, a pioneering shape alignment and recovery network that achieves category-level 6D object pose and size estimation from a single image without requiring real pose-annotated training data—a breakthrough that has garnered 86 citations. He has also advanced human-robot interaction through innovative works on fine-grained language instructions for grasping (FLarG) and freehand sketch-based grasp detection, bridging the gap between abstract human communication and robotic action. His work on weakly-supervised liquid perception (PourIt!) and open-ended manipulation using large language models (Polaris, WALL-E) demonstrates a sustained commitment to creating more intuitive, capable robotic systems. With over 160 citations across his top publications, Fu’s research is shaping the future of embodied AI and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 3Language Guided Robotic Grasping with Fine-Grained Instructions16 citations · 2023
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- 10WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model3 citations · 2023