Chuhan Shi
Papers
1
Total Citations
1
H-Index
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About
Chuhan Shi is a researcher advancing the field of human-robot interaction, with a focus on preference-based learning and human-centered AI systems. Their key research areas include robot trajectory preference elicitation, human labeling assistance, and interactive machine learning. Shi’s major contribution is the development of FARPLS (Feature-Augmented Robot Trajectory Preference Labeling System), a novel framework that helps human labelers more effectively digest and identify preferences when comparing robot task trajectories. This work addresses a critical bottleneck in aligning robot objectives with human values—traditionally, comparison-based preference labeling systems offer limited support for labelers to make nuanced judgments. By augmenting trajectories with salient features, FARPLS enhances the efficiency and accuracy of preference elicitation, directly improving how robots learn from human feedback. Though early in their career, Shi’s work has already garnered attention for its practical impact on making robot learning more interpretable and aligned with human intent. Their research sits at the intersection of robotics, human-computer interaction, and AI ethics, promising to shape how future autonomous systems are trained to respect human preferences.
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Top Papers
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