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Task-Oriented Connectivity for Networked Robotics with Generative AI and Semantic Communications

Peizheng Li, Adnan Aijaz

TL;DR

This work tackles the challenge of autonomous, efficient networked robotics in industrial settings by unifying goal-oriented semantic communication with a Generative AI agent within a semantic-aware AI-native network. The proposed co-working architecture leverages a semantic Open RAN backbone and a GenAI-agent to coordinate task decomposition, resource allocation, and in-network decision-making, while SemCom minimizes data transmission without sacrificing task-relevant information. A multi-robot anomaly-detection use-case demonstrates that semantic compression can reduce data traffic by up to a factor of ~40 while maintaining classification performance, and the GenAI-agent provides adaptive coordination and network-reconfiguration guidance. The findings highlight a scalable path toward robust, real-time industrial automation with reduced bandwidth requirements and enhanced autonomy, setting the stage for real-world deployments in smart factories.

Abstract

The convergence of robotics, advanced communication networks, and artificial intelligence (AI) holds the promise of transforming industries through fully automated and intelligent operations. In this work, we introduce a novel co-working framework for robots that unifies goal-oriented semantic communication (SemCom) with a Generative AI (GenAI)-agent under a semantic-aware network. SemCom prioritizes the exchange of meaningful information among robots and the network, thereby reducing overhead and latency. Meanwhile, the GenAI-agent leverages generative AI models to interpret high-level task instructions, allocate resources, and adapt to dynamic changes in both network and robotic environments. This agent-driven paradigm ushers in a new level of autonomy and intelligence, enabling complex tasks of networked robots to be conducted with minimal human intervention. We validate our approach through a multi-robot anomaly detection use-case simulation, where robots detect, compress, and transmit relevant information for classification. Simulation results confirm that SemCom significantly reduces data traffic while preserving critical semantic details, and the GenAI-agent ensures task coordination and network adaptation. This synergy provides a robust, efficient, and scalable solution for modern industrial environments.

Task-Oriented Connectivity for Networked Robotics with Generative AI and Semantic Communications

TL;DR

This work tackles the challenge of autonomous, efficient networked robotics in industrial settings by unifying goal-oriented semantic communication with a Generative AI agent within a semantic-aware AI-native network. The proposed co-working architecture leverages a semantic Open RAN backbone and a GenAI-agent to coordinate task decomposition, resource allocation, and in-network decision-making, while SemCom minimizes data transmission without sacrificing task-relevant information. A multi-robot anomaly-detection use-case demonstrates that semantic compression can reduce data traffic by up to a factor of ~40 while maintaining classification performance, and the GenAI-agent provides adaptive coordination and network-reconfiguration guidance. The findings highlight a scalable path toward robust, real-time industrial automation with reduced bandwidth requirements and enhanced autonomy, setting the stage for real-world deployments in smart factories.

Abstract

The convergence of robotics, advanced communication networks, and artificial intelligence (AI) holds the promise of transforming industries through fully automated and intelligent operations. In this work, we introduce a novel co-working framework for robots that unifies goal-oriented semantic communication (SemCom) with a Generative AI (GenAI)-agent under a semantic-aware network. SemCom prioritizes the exchange of meaningful information among robots and the network, thereby reducing overhead and latency. Meanwhile, the GenAI-agent leverages generative AI models to interpret high-level task instructions, allocate resources, and adapt to dynamic changes in both network and robotic environments. This agent-driven paradigm ushers in a new level of autonomy and intelligence, enabling complex tasks of networked robots to be conducted with minimal human intervention. We validate our approach through a multi-robot anomaly detection use-case simulation, where robots detect, compress, and transmit relevant information for classification. Simulation results confirm that SemCom significantly reduces data traffic while preserving critical semantic details, and the GenAI-agent ensures task coordination and network adaptation. This synergy provides a robust, efficient, and scalable solution for modern industrial environments.

Paper Structure

This paper contains 24 sections, 7 figures.

Table of Contents

  1. Introduction
  2. Preliminary
  3. Networked robotics system
  4. AI-native network
  5. SemCom
  6. Semantic-aware connectivity
  7. GenAI-agent
  8. Synergistic co-working of GAI-agent and SemCom for Robotic System
  9. Overall architecture
  10. Workflow of GenAI-agent
  11. Multi-robot system for anomaly detection
  12. Use-case model
  13. Robotics agents
  14. Communication framework
  15. Operational scenarios
  16. ...and 9 more sections

Figures (7)

  • Figure 1: Scenarios of cobot operation for goods moving farnham2021umbrella
  • Figure 2: Illustration of the co-working framework that incorporates GenAI-agent and SemCom for the networked robotics system
  • Figure 3: Workflow of the GAI-agent
  • Figure 4: Example of initial prompts to the GAI-agent and its response (partial response omitted for brevity)
  • Figure 5: Illustration of VAE used for robot observation reconstruction
  • ...and 2 more figures