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

2025-04-28

AI Summary

This paper addresses reliability, safety, and trustworthiness challenges in autonomous robotic systems arising from imbalanced human–automation collaboration. To tackle this, we propose a systematic research framework guided by Human-Centered AI (HCAI). Using bibliometric analysis via SciMAT and VOSviewer on Scopus data, we map thematic evolution and emerging trends in the field. Crucially, we present the first in-depth mapping of HCAI principles onto IBM’s MAPE-K adaptive control architecture, establishing a rigorous translational bridge from theory to engineering practice. Our analysis reveals AI’s pivotal role in adaptive behavior generation and human–robot collaboration, distills key HCAI-driven design dimensions for robotic autonomy, and introduces an actionable architectural mapping framework. This work provides both a methodological foundation and practical guidance for developing safe, trustworthy, and collaborative intelligent robotic systems.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Balancing human control and automation in robotics
• Ensuring reliability, safety, and trustworthiness in autonomous robots
• Mapping HCAI research trends to real-world robotic systems

Innovation

• Human-Centered AI balances automation and human control
• Bibliometric analysis using SciMAT and VOSViewer tools
• IBM MAPE-K architecture integrates HCAI for robotics