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.
arXiv:2608. 06227v1 Announce Type: cross Abstract: Despite advances in artificial intelligence (AI) across multiple sectors, today's AI tools, including deep learning and generative AI, still fail when embedded into physical systems, such as robots and vehicles operating under real-world physical laws.
A quick guide to separating Physical AI from world models, embodied AI, physics AI, and digital twins The post Physical AI: What It Is and What It Is Not appeared first on Towards Data Science .
arXiv:2606. 00090v1 Announce Type: cross Abstract: Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential actions.
The paper surveys Generative Physical Artificial Intelligence (GPAI), a field where large foundation models are integrated with physical robots. It introduces a taxonomy of five approaches—Robot Foundation Models, Vision‑Language Action models, Large Behavior Models, Diffusion Policy Models, and World Foundation Models—and discusses how they complement each other across domains such as autonomous vehicles, industrial automation, healthcare robotics, and humanoid systems. The review highlights performance gains, data‑efficient learning, sim‑to‑real transfer, edge‑compatible architectures, and safety frameworks as key research directions.
arXiv:2607. 11689v1 Announce Type: cross Abstract: Artificial general intelligence ultimately requires agents that can reason and act in the physical world.
The paper introduces the concept of Physical AI—systems that understand and act within the physical world, where interactions are continuous, uncertain, and irreversible. It surveys trustworthy principles specific to Physical AI, outlines the role of physics in AI, and maps the end‑to‑end life cycle across five core stages, culminating in the Trustworthy Physical AI Operationalization (T‑PAIO) and the broader Trustworthy Physical AI (T‑PAI) framework.
The integration of large-scale foundation models with physical embodiments has led to significant advancements in robotics known as Generative Physical Artificial Intelligence (GPAI). These agentic AI...
Artificial general intelligence ultimately requires agents that can reason and act in the physical world. Action models, vision-language-action policies, and world models have advanced this goal, while World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences.
arXiv:2607. 22877v1 Announce Type: new Abstract: With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world.
arXiv:2608. 05792v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop intelligent system, a paradigm we term AISAC.
The paper defines an autonomous agent as a system that maintains task goals and viability within explicit identity and authority boundaries, continuously acting with world and self-models, and adjusting control mechanisms through feedback. It traces the evolution from homeostasis to biological autonomy and identifies six operationalization lines, culminating in a one-boundary, four-loop framework that organizes task execution, self-maintenance, metacontrol, and governed learning. Five simulation experiments test permission constraints, road-closure response, health maintenance, capability re-estimation, and cross-task learning, offering preliminary evidence for the feasibility of these mechanisms while noting the need for broader testing.
arXiv:2507.04356v3 Announce Type: replace-cross Abstract: Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This...