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.

翻译：机器人技术、先进通信网络与人工智能（AI）的融合，有望通过全自动智能运营实现产业变革。本文提出一种新颖的机器人协同工作框架，该框架在语义感知网络下，将面向目标的语义通信（SemCom）与生成式人工智能（GenAI）智能体相统一。SemCom 优先实现机器人及网络间有意义信息的交换，从而降低开销与延迟。与此同时，GenAI 智能体利用生成式 AI 模型来解析高层任务指令、分配资源，并适应网络与机器人环境的动态变化。这种智能体驱动的范式带来了全新的自主性与智能化水平，使得网络化机器人能够以最少的人力干预执行复杂任务。我们通过一个多机器人异常检测用例仿真验证了所提方法，其中机器人负责检测、压缩并传输相关信息以进行分类。仿真结果证实，SemCom 在保留关键语义细节的同时显著降低了数据流量，而 GenAI 智能体则确保了任务协调与网络适应性。这种协同作用为现代工业环境提供了一个鲁棒、高效且可扩展的解决方案。