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

arXiv: https://arxiv.org/abs/2503.06771 · HTML: https://mlsanity.com/paper/2503.06771/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach](https://mlsanity.com/paper/2402.07573/) (2024)
- [Open RAN meets Semantic Communications: A Synergistic Match for Open, Intelligent, and Knowledge-Driven 6G](https://mlsanity.com/paper/2310.09951/) (2023)
- [Large Generative AI Models meet Open Networks for 6G: Integration, Platform, and Monetization](https://mlsanity.com/paper/2410.18790/) (2024)
- [Toward Real-Time Wireless Control of Mobile Platforms for Future Industrial Systems](https://mlsanity.com/paper/1907.01979/) (2019)

## Built on by (most cited first, 2 of 2)

- [Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies](https://mlsanity.com/paper/2603.16952/) (2026)
- [Latency-aware Human-in-the-Loop Reinforcement Learning for Semantic Communications](https://mlsanity.com/paper/2602.15640/) (2026)

## Abstract (arXiv)

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.