Imagine a robot learning to navigate a complex world not through physical trial and error, but within the boundless realm of text. This is the fascinating premise behind BeSimulator, a groundbreaking approach that leverages the power of large language models (LLMs) to create dynamic, text-based simulations for robots. Traditional robot simulators, while visually impressive, often grapple with high computational costs and limited adaptability. BeSimulator tackles these challenges by shifting the focus from precise physical modeling to the core logic of robot behavior—how a robot thinks and reacts in different situations. This innovative framework generates virtual environments described entirely through text, enabling efficient, long-horizon simulations. BeSimulator mirrors the human thought process through its “consider-decide-capture-transfer” methodology. Imagine the robot contemplating the feasibility of an action, like picking up a toy, and then determining the consequences of that action within the text-based world. This intricate reasoning process is further enhanced by code-driven reasoning, ensuring accurate calculations when dealing with numerical values. But what about mistakes? BeSimulator cleverly uses a “reflective feedback” mechanism. If the LLM produces an illogical output, the system prompts it to reflect and correct itself, much like a human learning from errors. This iterative refinement process dramatically improves the simulation's accuracy. BeSimulator’s prowess was rigorously tested on BTSIMBENCH, a custom-built benchmark designed to evaluate behavior trees—decision-making structures used in robotics. The results? Significant improvements in simulation accuracy across various LLMs, showcasing the power of BeSimulator. This novel approach opens exciting doors for robotics research and development. Imagine training robots to perform complex tasks in diverse simulated scenarios, accelerating their learning curve and reducing the need for costly physical prototypes. BeSimulator promises not only faster development but also the ability to simulate scenarios too complex or dangerous to replicate in the real world, ushering in a new era of efficient and adaptable robot training.