The integration of language models into robotics enhances executive decision-making capabilitiesand establishes a foundation for world model grounding, opening new avenues in robotic navigation. Advanced models like GPT, Mistral and Gemini enable robots to make well-informed decisions by processing data from Visual Question Answering (VQA) models and real-time object detectors, leveraging their capabilities for cutting-edge exploration. Moreover, these models can compress observations and actions into natural language, forming a memory that guides future navigation. We propose a comprehensive pipeline that employs a ROS-Unreal interface to evaluate a language model-driven frontier-point exploration strategy. This approach showcases intuitive reasoning, leverages past experiences for future navigation, and generates a human-readable record of the decision-making process.