用生成式AI与语义通信让机器人网络更智能高效
Task-Oriented Connectivity for Networked Robotics with Generative AI and Semantic Communications
- 用语义通信传递关键信息，降低通信开销
- 生成式AI代理实现任务理解与动态资源分配
- 适合工业自动化中多机器人协同场景
机器人、先进通信网络与人工智能的融合有望通过全自动化和智能化操作重塑产业。本文提出一种新型协作框架，将目标导向的语义通信（SemCom）与生成式AI（GenAI）代理结合，构建语义感知网络。SemCom优先传输有意义的信息，降低开销与延迟；GenAI代理利用生成式模型解析高层任务指令，进行资源分配并适应网络与机器人环境的动态变化。该代理驱动范式提升了自主性与智能水平，使网络化机器人在极少人工干预下完成复杂任务。通过多机器人异常检测仿真验证，结果表明SemCom显著减少数据流量，同时保留关键语义信息，GenAI代理保障任务协调与网络自适应。该协同机制为现代工业环境提供了一种鲁棒、高效且可扩展的解决方案。
原文摘要 · Abstract (English)
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.
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