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Integrating Artificial General Intelligence into Robotic Systems: A Pathway Toward Superintelligent Autonomous Machines

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Preprints.org
DOI
10.20944/preprints202603.1607.v1

The Artificial Intelligence (AI) and robots are at a high level, which has led to the new point of intersection and is changing the future of autonomous systems. Particularly, the integration of Artificial General Intelligence (AGI) into the architecture of robotic systems is a significant step towards development of superintelligent robots capable of conducting human reasoning, learning, and making decisions in different settings. This paper is an analysis of the technological, social and ethical implications of AGI robotics integration, the ability to transform, and the challenges that it presents.The discussions in this paper rely on the narrative review method of a qualitative research to analyze the current advancements and future prospects of computer science, engineering, economics, and policy studies through interdisciplinary literature. This review concludes that AGI-motivated robotics can enhance adaptability, autonomy, and efficiency by a substantial degree in the complex real-world task of healthcare, manufacturing, and transportation. However, the outcomes also reveal occurrence of severe problems related to system reliability, algorithm bias, transparency and control particularly upon the increase to autonomy and intelligence of systems.Furthermore, the transition toward superintelligent autonomous machines introduces profound governance challenges, including accountability, ethical alignment, and global regulatory coordination. The paper concludes that despite the unprecedented opportunities of innovation and social advancements, AGI-integrated robotics require well-developed safety mechanisms, humanist design ideas, and reactive policy instruments to be developed. The primary issue that the future research must face is explainability, sustainability, and equitable access to ensure that the benefits of superintelligent systems are maximised and the threats are minimised productively.

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