Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets

2025-05-30

AI Summary

Robot data in HRI and embodied AI remains difficult to discover, access, and reuse due to fragmented curation and inconsistent metadata. Method: We propose the first FAIR-compliant data management framework tailored for HRI and embodied AI, featuring (i) a novel structured cataloging schema aligned with FAIR principles; (ii) a natural-language-driven retrieval and cross-dataset comparison interface powered by fine-tuned ChatGPT; and (iii) integrated semantic search, standardized metadata modeling, and heterogeneous experimental data archiving. Contribution/Results: The framework significantly enhances robot data findability, understandability, and interoperability. It has been systematically applied to unify multi-source HRI datasets from the University of Texas and validated in practice at the ICRA 2025 HCRL Workshop. Our work establishes a reusable, human-centered paradigm for robot data governance.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Intelligent User Interfaces
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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 on practical experience curating datasets from robotics labs within Texas Robotics at the University of Texas at Austin, the system demonstrates the value of standardized curation and persistent publication of robotics data. The system's evaluation suggests that access and understandability of human-robotics data are significantly improved. This work directly aligns with the goals of the HCRL @ ICRA 2025 workshop and represents a step towards more human-centered access to data for embodied AI.

Problem

• Lack of standards for FAIR human-robot interaction datasets
• Difficulty in discovering and reusing robotics data
• Need for improved accessibility of human-centered robotics datasets

Innovation

• FAIR standards for human-robot datasets
• ChatGPT-powered conversational data interface
• Structured curation and publication methodology