Austin Narcomey
Papers
2
Total Citations
9
H-Index
2
About
Austin Narcomey is a researcher advancing human-robot interaction (HRI) through accessible, data-driven methods. His work centers on two key areas: leveraging implicit human feedback to improve robot learning, and designing low-cost robotic platforms for real-world deployment. In his highly cited 2023 paper, “Self-Annotation Methods for Aligning Implicit and Explicit Human Feedback in Human-Robot Interaction” (7 citations), Narcomey tackles the challenge of interpreting subtle, non-verbal cues from users—such as gaze or hesitation—to refine robot behavior without burdening people with explicit training tasks. This work offers scalable solutions for making robots more intuitive and responsive. Complementing this, his 2024 paper “Shutter: A Low-Cost and Flexible Social Robot Platform for In-the-Wild Deployments” (2 citations) introduces an open-source, affordable robot designed for studying HRI outside controlled labs. By addressing the gap between expensive, proprietary systems and the need for naturalistic studies, Narcomey’s contributions enable broader, more equitable access to robotics research. His focus on practical, user-centered design and real-world validation positions him as a rising voice in making HRI both scientifically rigorous and socially impactful.
Research Focus
Key Achievements
Top Papers
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