Physical Artificial Intelligence: From Perception to Autonomy - Foundations, Learning, and Social Implications

Abstract

This article examines the conceptual foundations, technical architecture, learning strategies, and social implications of Physical Artificial Intelligence (Physical AI) or Embodied AI. Unlike traditional AI systems, which operate within the boundaries of digital environments, Physical AI integrates perception, cognition, and action to exist as embodied agents in the real world. The article presents sensor technologies, deep learning architectures, simulation-to-reality training strategies, and multimodal self-supervised learning as key elements for adaptive autonomy. Practical applications in the fields of medicine, manufacturing, and public safety are introduced to illustrate current advances. Finally, the article discusses the economic, social, and ethical risks associated with autonomous decision-making in machines and humanoid robots equipped with embedded AI, arguing that the large-scale adoption of Physical AI represents a societal shift whose consequences must be critically examined.

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