Physical AI: Concepts, Techniques, and Challenges
Read the original on arXiv AI →The monograph defines physical AI as artificial systems that perceive, model, and act upon the physical world through continuous feedback under real‑world constraints, distinguishing it from digital AI, embodied AI, cyber‑physical systems, and digital twins. It surveys the technical landscape—covering physics, control, cognition, learning, and reasoning—and reviews key methodologies such as multimodal perception, simulation, world models, embodied foundation models, motor‑control systems, multi‑agent coordination, and hardware‑software co‑design. The work also explores applications in robotics, manufacturing, infrastructure, scientific experimentation, and autonomous services, while addressing challenges like sim‑to‑real transfer, reliability, real‑time execution, evaluation, safety, governance, and long‑term deployment.
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