The usage of Large Language Models (LLMs) has increased recently, not only due to the significant improvements in their accuracy but also because of the use of the quantization that allows running these models without intense hardware requirements. As a result, the LLMs have proliferated. It implies the creation of a great variety of LLMs with different capabilities. This way, this paper proposes the integration of LLMs in cognitive architectures for autonomous robots. Specifically, we present the design, development and deployment of the llama\_ros tool that allows the easy use and integration of LLMs in ROS 2-based environments, afterward integrated with the state-of-the-art cognitive architecture MERLIN2 for updating a PDDL-based planner system. This proposal is evaluated quantitatively and qualitatively, measuring the impact of incorporating the LLMs in the cognitive architecture.

翻译：近年来，大型语言模型（LLMs）的使用日益增加，这不仅得益于其准确性的显著提升，还因为量化技术的应用使得这些模型无需严苛的硬件要求即可运行。由此，LLMs得以广泛普及，催生了多种具备不同能力的大语言模型。基于此，本文提出将LLMs集成到自主机器人的认知架构中。具体而言，我们介绍了llama_ros工具的设计、开发与部署，该工具可便捷地将LLMs用于基于ROS 2的环境，并随后与当前最先进的认知架构MERLIN2集成，以更新基于PDDL的规划系统。本方案经过了定量与定性评估，重点衡量了将LLMs纳入认知架构所带来的影响。