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

2025-03-09

AI Summary

To address inefficient semantic information transmission, high communication overhead, and insufficient adaptability to dynamic environments in networked robotic collaboration, this paper proposes a novel collaborative paradigm synergizing semantic communication and generative AI agents. Our method constructs a semantic-aware network integrating goal-oriented semantic communication (SemCom), lightweight semantic encoding, task instruction parsing, and multi-robot coordinated control—enabling a paradigm shift from raw data transmission to closed-loop, high-level task semantics. Experimental results in multi-robot anomaly detection simulations demonstrate significant improvements: 62% reduction in communication traffic, 98.3% key semantic fidelity, 41% higher task completion rate, and millisecond-scale network reconfiguration. To the best of our knowledge, this is the first work to unify semantic-level resource scheduling with dynamic environmental adaptation.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Agent Communication
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Integrates semantic communication with Generative AI for networked robotics.
• Reduces data traffic and latency in robot communication networks.
• Enhances autonomy and task coordination in dynamic robotic environments.

Innovation

• Semantic communication reduces data traffic.
• Generative AI interprets and adapts tasks.
• Agent-driven framework enhances robotic autonomy.