Task-Driven Semantic Collaborative Communication Helps Multi-Robot Systems with Embodied Intelligence

In recent years, AI Agents centered on large language models (LLMs) have flourished, providing a prerequisite for the robots with embodied intelligence in the new era. Using LLM as the brain of AI agents can endow robots with powerful abilities of understanding, reasoning and decision- making. Moreover, semantic communication is considered as a key technology to drive the development of multi-robot collaborative communication, which provides a novel solution for multi-robot systems. However, current multi-robot systems suffer from several problems when dealing with complex tasks, including chaotic agent cooperation, semantic relaying distortion, and inefficient topology, combined with the powerful autonomy of LLMs. To address these issues, we propose a task-driven semantic autonomous collaboration framework with AI Agent in multi-robot system. First, we introduce the different roles to achieve an efficient autonomous decomposition by AI Agents facing complex tasks through the dynamic iterative leadership election. Second, we propose a cooperative forwarding scheme according to the relay AI agent based on the prediction of robot behavior patterns and hybrid communication mechanisms. Third, a dynamic task-driven topology optimization in multi-robot systems by graph neural network (GNN) is proposed to achieve fine-tuning of the semantic communication topology through sparse regularization. Finally, the results of the simulation experiment show that the task execution accuracy of our method on the GSM8K dataset is 6\% higher than that of LLM-Debate, verifying the effectiveness and feasibility of this framework in multi-robot cooperative communication for complex tasks in the future.