Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets
Xingru Zhou, Sadanand Modak, Yao-Cheng Chan, Zhiyun Deng, Luis Sentis, Maria Esteva

TL;DR
This paper introduces a system that standardizes the curation and publication of FAIR human-robot datasets and uses a ChatGPT interface for natural language exploration and retrieval, improving data accessibility and usability.
Contribution
It presents a structured methodology for FAIR dataset curation and a ChatGPT-based interface for natural language data exploration in robotics.
Findings
Enhanced discoverability of robotics datasets
Improved data access and understanding
Facilitated comparison of datasets
Abstract
The rapid growth of AI in robotics has amplified the need for high-quality, reusable datasets, particularly in human-robot interaction (HRI) and AI-embedded robotics. While more robotics datasets are being created, the landscape of open data in the field is uneven. This is due to a lack of curation standards and consistent publication practices, which makes it difficult to discover, access, and reuse robotics data. To address these challenges, this paper presents a curation and access system with two main contributions: (1) a structured methodology to curate, publish, and integrate FAIR (Findable, Accessible, Interoperable, Reusable) human-centered robotics datasets; and (2) a ChatGPT-powered conversational interface trained with the curated datasets metadata and documentation to enable exploration, comparison robotics datasets and data retrieval using natural language. Developed based…
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Taxonomy
TopicsAnatomy and Medical Technology · Robotics and Automated Systems · Advanced Neural Network Applications
