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

arXiv: https://arxiv.org/abs/2504.19848 · HTML: https://mlsanity.com/paper/2504.19848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Human-Centered Artificial Intelligence and Machine Learning](https://mlsanity.com/paper/1901.11184/) (2019)
- [Artificial Intelligence for Long-Term Robot Autonomy: A Survey](https://mlsanity.com/paper/1807.05196/) (2018)
- [The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems](https://mlsanity.com/paper/2105.04408/) (2021)
- [Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)](https://mlsanity.com/paper/2006.02366/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Mentalistic Interface for Probing Folk-Psychological Attribution to Non-Humanoid Robots](https://mlsanity.com/paper/2603.25646/) (2026)

## Abstract (arXiv)

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.