This paper introduces LLM-MARS, first technology that utilizes a Large Language Model based Artificial Intelligence for Multi-Agent Robot Systems. LLM-MARS enables dynamic dialogues between humans and robots, allowing the latter to generate behavior based on operator commands and provide informative answers to questions about their actions. LLM-MARS is built on a transformer-based Large Language Model, fine-tuned from the Falcon 7B model. We employ a multimodal approach using LoRa adapters for different tasks. The first LoRa adapter was developed by fine-tuning the base model on examples of Behavior Trees and their corresponding commands. The second LoRa adapter was developed by fine-tuning on question-answering examples. Practical trials on a multi-agent system of two robots within the Eurobot 2023 game rules demonstrate promising results. The robots achieve an average task execution accuracy of 79.28% in compound commands. With commands containing up to two tasks accuracy exceeded 90%. Evaluation confirms the system's answers on operators questions exhibit high accuracy, relevance, and informativeness. LLM-MARS and similar multi-agent robotic systems hold significant potential to revolutionize logistics, enabling autonomous exploration missions and advancing Industry 5.0.

翻译：本文介绍了LLM-MARS，这是首个利用基于大语言模型的人工智能技术实现多智能体机器人系统的技术。LLM-MARS支持人与机器人之间的动态对话，使机器人能够根据操作员指令生成行为，并针对其行为的相关问题提供信息丰富的回答。该系统基于Transformer架构的大语言模型构建，通过微调Falcon 7B模型实现。我们采用多模态方法，针对不同任务使用LoRa适配器：第一个LoRa适配器通过微调基础模型（基于行为树及其对应指令的示例）开发；第二个则通过微调问答示例实现。在Eurobot 2023比赛规则框架下的双机器人多智能体系统实际测试中，该系统展现出良好性能：机器人执行复合指令的平均任务准确率达79.28%，且当指令包含两个以内子任务时准确率超过90%。评估证实，该系统对操作员问题的回答具有高准确性、相关性和信息丰富度。LLM-MARS及类似的多智能体机器人系统有望彻底改变物流领域，推动自主探索任务，并促进工业5.0的发展。