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 embodied, material 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. Thanks to its soft, sensor supported joints, it can physically sense how much weight a person can bear. It learns from resistance, movement, and body language to precisely regulate its strength and recognizes when it needs to actively intervene or hold back. 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. In general, this work is intended to contribute to the theoretical and methodological foundation of a new generation of intelligent systems that act physically, learn, and act responsibly in the world. The principles formulated here are intended to serve as a guide for researchers and engineers to further develop Physical AI from an abstract idea into a practical design concept.