Recent advancements in large language models and visual language models are significantly impacting video understanding and scene interpretation, especially within the field of mobile robotics. These technologies enhance the ability of robotic agents to perceive their surroundings and interact more effectively without requiring prior training on specific tasks. The integration of zero-shot scene interpretation capabilities on edge devices allows mobile robots to make rational decisions in real time, improving their autonomy and adaptability. This progress reflects a broader trend in artificial intelligence research focused on deploying sophisticated models in resource-constrained environments. By leveraging these models, mobile robotics can achieve more nuanced environmental awareness, which is crucial for practical applications. The ongoing evaluation of these approaches from laboratory settings to real-world scenarios underscores the potential for transformative impacts in robotics and AI-driven perception. This development aligns with recent contextual research emphasizing the application of large language models in diverse, real-time operational contexts.