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  3. A Roadmap For Improving Data Quality Through Standards For Collaborative Intelligence In Human-robot Applications.
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A roadmap for improving data quality through standards for collaborative intelligence in human-robot applications.

Shakra Mehak1,2, Inês F Ramos3, Keerthi Sagar4

  • 1Pilz Ireland Industrial Automation, Cork, Ireland.

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|December 27, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Ensuring data quality is vital for safety-critical collaborative intelligence (CI) systems. This study addresses data quality challenges in human-robot interaction (HRI) for industrial applications.

Keywords:
ISO 8000ISO standardartificial intelligencecollaborative intelligencehuman machine interactionhuman robot interaction (HRI)machine learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Collaborative intelligence (CI) systems are safety-critical, relying on reliable human-machine interactions to prevent harm.
  • The increasing data-driven nature of CI applications necessitates high-quality data for robust performance in unpredictable environments.
  • Adherence to data quality standards is crucial for advancing CI systems in industrial settings.

Purpose of the Study:

  • To identify and address data quality challenges in industrial CI applications, specifically within human-robot interaction (HRI).
  • To present two use cases demonstrating data collection and analysis in HRI for improved CI system reliability.
  • To propose a hybrid standardization approach for multi-modal HRI data acquisition.

Main Methods:

  • Development of a framework for quantifying human and robot performance in naturalistic robot learning scenarios.
  • Implementation of real-time user state monitoring for adaptive multi-modal teleoperation systems.
  • Derivation of a hybrid standardization approach from existing ISO data quality standards.

Main Results:

  • The study highlights specific data quality challenges encountered in industrial HRI data collection.
  • The use cases demonstrate practical methods for collecting and utilizing HRI data to enhance CI system adaptability and performance.
  • A novel hybrid standardization framework is proposed to manage multi-modal HRI data quality.

Conclusions:

  • Addressing data quality is paramount for the safe and effective deployment of CI systems in industry.
  • The presented use cases and proposed standardization offer valuable insights for improving data acquisition and management in HRI.
  • The findings contribute to the advancement of safety-critical CI applications through better data quality practices.