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Human-Centered AI and Autonomy in Robotics: Insights from a Bibliometric Study

Simona Casini, Pietro Ducange, Francesco Marcelloni, Lorenzo Pollini

TL;DR

This study addresses how AI advances shape self-adaptive autonomy in robotics while centering human users. It combines SciMAT and VOSviewer bibliometric analysis of 2,564 Scopus papers from 2000–2024 and links findings to IBM's MAPE-K architecture to bridge theory with real-world system design. Key results show deep learning and reinforcement learning as core enablers, with rising attention to perception, SLAM, and human-robot interaction under Human-Centered AI principles; the MAPE-K mapping highlights Plan and Knowledge as critical for mission decomposition and transparency. The findings inform practice and policy by aligning research with trustworthy, human-centric autonomous robotics and European priorities.

Abstract

The development of autonomous robotic systems offers significant potential for performing complex tasks with precision and consistency. Recent advances in Artificial Intelligence (AI) have enabled more capable intelligent automation systems, addressing increasingly complex challenges. However, this progress raises questions about human roles in such systems. Human-Centered AI (HCAI) aims to balance human control and automation, ensuring performance enhancement while maintaining creativity, mastery, and responsibility. For real-world applications, autonomous robots must balance task performance with reliability, safety, and trustworthiness. Integrating HCAI principles enhances human-robot collaboration and ensures responsible operation. This paper presents a bibliometric analysis of intelligent autonomous robotic systems, utilizing SciMAT and VOSViewer to examine data from the Scopus database. The findings highlight academic trends, emerging topics, and AI's role in self-adaptive robotic behaviour, with an emphasis on HCAI architecture. These insights are then projected onto the IBM MAPE-K architecture, with the goal of identifying how these research results map into actual robotic autonomous systems development efforts for real-world scenarios.

Human-Centered AI and Autonomy in Robotics: Insights from a Bibliometric Study

TL;DR

This study addresses how AI advances shape self-adaptive autonomy in robotics while centering human users. It combines SciMAT and VOSviewer bibliometric analysis of 2,564 Scopus papers from 2000–2024 and links findings to IBM's MAPE-K architecture to bridge theory with real-world system design. Key results show deep learning and reinforcement learning as core enablers, with rising attention to perception, SLAM, and human-robot interaction under Human-Centered AI principles; the MAPE-K mapping highlights Plan and Knowledge as critical for mission decomposition and transparency. The findings inform practice and policy by aligning research with trustworthy, human-centric autonomous robotics and European priorities.

Abstract

The development of autonomous robotic systems offers significant potential for performing complex tasks with precision and consistency. Recent advances in Artificial Intelligence (AI) have enabled more capable intelligent automation systems, addressing increasingly complex challenges. However, this progress raises questions about human roles in such systems. Human-Centered AI (HCAI) aims to balance human control and automation, ensuring performance enhancement while maintaining creativity, mastery, and responsibility. For real-world applications, autonomous robots must balance task performance with reliability, safety, and trustworthiness. Integrating HCAI principles enhances human-robot collaboration and ensures responsible operation. This paper presents a bibliometric analysis of intelligent autonomous robotic systems, utilizing SciMAT and VOSViewer to examine data from the Scopus database. The findings highlight academic trends, emerging topics, and AI's role in self-adaptive robotic behaviour, with an emphasis on HCAI architecture. These insights are then projected onto the IBM MAPE-K architecture, with the goal of identifying how these research results map into actual robotic autonomous systems development efforts for real-world scenarios.
Paper Structure (9 sections, 8 figures)

This paper contains 9 sections, 8 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. Research question and filters
  4. Science mapping tools
  5. Descriptive analysis
  6. Historical analysis
  7. Results summary and Future Directions
  8. MAPE-K Architecture for HCAI-based Autonomous Robotic Systems
  9. Conclusions

Figures (8)

  • Figure 1: SciMAT Maps description - Details in Cobo2011.
  • Figure 2: Evolution map - The average number of citations is used as the bibliometric indicator (see subsection \ref{['subsec: tools']}). Consider, for example Mapping cluster. Over the three decades, the cluster has progressively evolved, particularly in the most recent period, into a more complex task domain, such as exploration and inspection, following its convergence with the reinforcement learning cluster.
  • Figure 3: Strategic diagram from 2000 to 2024. The average number of citations is used as the bibliometric indicator (see subsection II-B)
  • Figure 4: VOSViewer network visualization
  • Figure 5: MAPE-K framework with colours reflecting historical research interests based on bibliometric analysis.
  • ...and 3 more figures