Fundamentals of Physical AI

TL;DR

This paper establishes a theoretical framework for Physical AI, emphasizing embodied interaction, sensory perception, and learning as emergent from real-world physical engagement, contrasting with traditional symbolic AI.

Contribution

It introduces six fundamental principles forming a closed control loop for Physical AI, shifting the understanding of intelligence from symbolic processing to embodied experience.

Findings

Physical AI principles form a closed control loop involving energy, information, control, and context.

Learning is viewed as structural coupling change, not parameter adjustment.

The model is exemplified through an adaptive robot in a rehabilitation setting.

Abstract

This work will elaborate the fundamental principles of physical artificial intelligence (Physical AI) from a scientific and systemic perspective. The aim is to create a theoretical foundation that describes the physical embodiment, sensory perception, ability to act, learning processes, and context sensitivity of intelligent systems within a coherent framework. While classical AI approaches rely on symbolic processing and data driven models, Physical AI understands intelligence as an emergent phenomenon of real interaction between body, environment, and experience. The six fundamentals presented here are embodiment, sensory perception, motor action, learning, autonomy, and context sensitivity, and form the conceptual basis for designing and evaluating physically intelligent systems. Theoretically, it is shown that these six principles do not represent loose functional modules but rather act as a closed control loop in which energy, information, control, and context are in constant interaction. This circular interaction enables a system to generate meaning not from databases, but from physical experience, a paradigm shift that understands intelligence as an physical embodied process. Physical AI understands learning not as parameter adjustment, but as a change in the structural coupling between agents and the environment. To illustrate this, the theoretical model is explained using a practical scenario: An adaptive assistant robot supports patients in a rehabilitation clinic. This example illustrates that physical intelligence does not arise from abstract calculation, but from immediate, embodied experience. It shows how the six fundamentals interact in a real system: embodiment as a prerequisite, perception as input, movement as expression, learning as adaptation, autonomy as regulation, and context as orientation.