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About
Ujaan Das is a researcher advancing the frontier of preference-based learning in robotics, with a focus on aligning autonomous systems with human values. His key research areas include human-robot interaction, preference elicitation, and interactive machine learning. Das’s major contribution is the development of FARPLS—a Feature-Augmented Robot Trajectory Preference Labeling System—which addresses a critical bottleneck in preference-based learning: the cognitive load on human labelers. Traditional systems require users to compare raw trajectory pairs without support, often leading to inconsistent or unreliable labels. FARPLS introduces feature augmentation to help labelers digest, identify, and articulate their preferences more effectively, thereby improving the quality of human feedback for robot task alignment. Though published in 2024 and currently accumulating citations, this work represents a foundational step toward more intuitive and scalable human-robot collaboration. Das’s research is particularly notable for its human-centered design philosophy, bridging the gap between algorithmic learning and real-world usability. His work holds promise for applications in assistive robotics, autonomous driving, and personalized automation, where understanding nuanced human preferences is essential for safe and trustworthy deployment.
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