Task-Oriented Connectivity for Networked Robotics with Generative AI and Semantic Communications
This work addresses the need for scalable and intelligent robotic systems in industrial environments, representing an incremental advancement by integrating existing technologies like semantic communication and Generative AI.
The paper tackles the problem of enabling autonomous and efficient operations for networked robots by introducing a framework that combines semantic communication to reduce data traffic and a Generative AI agent for task coordination, validated in a simulation showing reduced data traffic while preserving semantic details.
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.