Papers
7
Total Citations
44
H-Index
5
About
Yen-Ling Kuo’s research sits at the intersection of grounded language understanding, social reasoning, and robotic manipulation, with a focus on enabling robots to interact with humans in more flexible and socially aware ways. Her major contributions include developing compositional networks that allow deep learning models to achieve systematic generalization in grounded language tasks—a key step toward human-like language flexibility. She has also advanced social interaction modeling by formalizing rich sociological theories into recursive Markov decision processes (MDPs), enabling robots to reason about nested social dynamics. In robotic manipulation, her work on Diff-Dagger introduces uncertainty estimation with diffusion policy to address out-of-distribution failures and compounding errors. Her most cited paper, “Compositional Networks Enable Systematic Generalization for Grounded Language Understanding” (13 citations), highlights her impact on compositional reasoning. Kuo’s work has been recognized for its integration of linguistic, social, and planning components, making her a notable figure in embodied AI and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1
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- 3Social Interactions as Recursive MDPs7 citations · 2021
- 4Incorporating Rich Social Interactions Into MDPs6 citations · 2022
- 5
- 6Compositional RL Agents That Follow Language Commands in Temporal Logic3 citations · 2021
- 7