Physical AI Agents: Integrating Cognitive Intelligence with Real-World Action
Fouad Bousetouane

TL;DR
This paper introduces Physical AI Agents that combine cognitive reasoning with physical actions, enabling real-world interaction across industries, and proposes a modular architecture and Ph-RAG framework for real-time decision-making.
Contribution
It presents a novel modular architecture for Physical AI Agents and the Ph-RAG design pattern, integrating physical interaction with industry-specific LLMs for diverse applications.
Findings
Demonstrated applications in autonomous vehicles and warehouse robotics.
Showed improved operational efficiency through physical interaction.
Validated the framework across multiple industry case studies.
Abstract
Vertical AI Agents are revolutionizing industries by delivering domain-specific intelligence and tailored solutions. However, many sectors, such as manufacturing, healthcare, and logistics, demand AI systems capable of extending their intelligence into the physical world, interacting directly with objects, environments, and dynamic conditions. This need has led to the emergence of Physical AI Agents--systems that integrate cognitive reasoning, powered by specialized LLMs, with precise physical actions to perform real-world tasks. This work introduces Physical AI Agents as an evolution of shared principles with Vertical AI Agents, tailored for physical interaction. We propose a modular architecture with three core blocks--perception, cognition, and actuation--offering a scalable framework for diverse industries. Additionally, we present the Physical Retrieval Augmented Generation…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Cognitive Science and Mapping · Cognitive Computing and Networks
