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

Integration of Large Language Models within Cognitive Architectures for Autonomous Robots