AI-IoT-Robotics Integration: Survey of Frameworks, Emerging Trends, and the Path Toward Connected Robotics

2026-05-31 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Internet of Things (IoT) & Connected Devices · Depth: Expert, quick

Summary

This survey synthesizes the state-of-the-art in Artificial Intelligence, the Internet of Things, and Robotics integration, addressing the current lack of unified design frameworks. It highlights the rapid convergence of these domains, where AI provides perception, IoT offers scalable sensing, and robotics delivers embodied actuation. The work emphasizes the emerging role of Small Language Models (SLMs) at the edge and Large Language Models (LLMs) in the cloud for distributed cognition and autonomous decision-making. A modular system architecture is proposed, aligning with these trends, while analyzing persistent gaps in interoperability and feedback control. The survey classifies existing work by integration depth and demonstrates how hybrid SLM-LLM systems, coupled with IoT infrastructure and robotic agents, can enhance real-time adaptation, scalability, and reliability. This provides a conceptual and technical roadmap for next-generation AI-IoT-Robotic ecosystems, paving the way for Connected Robotics and Physical AI.

Key takeaway

For AI Architects designing next-generation intelligent systems, this survey underscores the necessity of integrating AI, IoT, and Robotics into unified frameworks. You should prioritize modular architectures that incorporate hybrid Small Language Model (SLM) and Large Language Model (LLM) deployments, employing SLMs at the edge for real-time processing and LLMs in the cloud for complex reasoning. This approach will enhance your system's real-time adaptation, scalability, and reliability, guiding your development towards robust Connected Robotics and Physical AI solutions.

Key insights

Unified AI-IoT-Robotics frameworks are crucial for real-time, intelligent systems, utilizing SLMs at the edge and LLMs in the cloud.

Principles

• AI, IoT, and Robotics converge to form intelligent, context-aware systems.
• Hybrid SLM-LLM architectures enable distributed cognition.
• Modular design improves interpretability and adaptability in dynamic environments.

Method

The survey proposes a modular system architecture for AI-IoT-Robotics integration, analyzing interoperability gaps and classifying existing work by integration depth to guide future design.

In practice

• Deploy SLMs at the edge for real-time processing.
• Utilize LLMs in the cloud for complex decision-making.
• Design for hybrid SLM-LLM systems with IoT and robotics.

Topics

• AI-IoT-Robotics Integration
• Connected Robotics
• Physical AI
• Small Language Models
• Large Language Models
• Edge Computing
• Distributed Cognition

Best for: Research Scientist, AI Scientist, Robotics Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.