Linyi Yang
Papers
1
Total Citations
2
H-Index
1
About
Linyi Yang is a pioneering researcher at the intersection of natural language processing and robotics, whose work explores how language can bridge human intention and machine action. Her key research areas include human-robot interaction, scene understanding, and the application of large language models to embodied AI. Yang’s most notable contribution is the development of a human-in-the-loop robotic grasping framework that leverages BERT-based scene representations, enabling users to guide robotic manipulation through natural language commands in cluttered environments. This work, published in 2022, has garnered attention for its innovative integration of NLP and robotics, laying groundwork for more intuitive human-robot collaboration. While her citation count is still growing—reflecting the emerging nature of her field—her research stands out for its practical vision: making robots responsive to real-time verbal feedback. Yang’s work is particularly relevant for students and researchers interested in how language models can serve as interfaces for physical systems, and her approach signals a shift toward more interactive, user-centered robotics. Her achievements highlight the potential of cross-disciplinary methods to solve complex, real-world manipulation tasks.
Research Focus
Key Achievements
Top Papers
- 1Human-in-the-loop Robotic Grasping using BERT Scene Representation2 citations · 2022