Hannah Kuehn

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About

Hannah Kuehn is a researcher at the intersection of robotics and human-computer interaction, with a primary focus on how natural language can bridge the gap between human intent and machine learning. Her key research area centers on leveraging unstructured, free-form human feedback to improve robot task policies, moving beyond rigid, pre-programmed commands. In her most cited work, "Exploring Unstructured Language Feedback for Robot Learning" (2025), Kuehn conducted a qualitative study with 66 participants who provided crowd-sourced feedback on three distinct robotic tasks. This foundational study revealed critical insights into how humans naturally correct robot behavior, identifying patterns in feedback that can be used to train more adaptive and responsive AI systems. By systematically analyzing the nuances of human language—from direct commands to subtle suggestions—Kuehn is pioneering methods to make robot learning more intuitive and accessible. Her work is already influencing the design of interactive learning systems, demonstrating that the messy, unstructured nature of human communication can be a powerful tool for refining robotic performance.