Simulation plays a key role in automated robotics research supported by large language models (LLMs). However, existing simulators often require custom code or complex interfaces, creating a barrier to rapid prototyping and automated algorithm development. To this end, we propose the Intelligent Robot Simulator (IR-SIM), a lightweight skill-native navigation simulator designed for rapid scenario construction, benchmarking, and robot learning. In IR-SIM, scenarios are entirely defined by YAML configuration files that specify mobile robot kinematics, geometric collision checking, LiDAR sensing, visualization, and behavior modules. This design makes robotic simulation fully describable and reproducible, allowing scenarios to be generated and modified from text prompts through the proposed IR-SIM agent skills. The resulting scenarios can be used for automated benchmarking of navigation algorithms and for automated generation of training data for learning methods. Furthermore, IR-SIM provides bridges to high fidelity simulators and real world deployment, allowing users to validate their algorithms in more realistic settings after prototyping without extra coding. The experiments showcase the convenience and versatility of IR-SIM in multiple tasks: constructing navigation scenarios from natural language, training a collision avoidance policy, benchmarking social navigation policies, and bridging to high fidelity simulators and real world deployment. The project website is available at https://github.com/hanruihua/ir-sim.

翻译：仿真在大语言模型支持的自动化机器人研究中发挥着关键作用。然而，现有仿真器通常需要定制代码或复杂接口，对快速原型开发与自动化算法设计构成了障碍。为此，本文提出智能机器人仿真器IR-SIM，这是一种轻量级的技能原生导航仿真器，专为快速场景构建、基准测试与机器人学习而设计。在IR-SIM中，场景完全由YAML配置文件定义，这些文件规定了移动机器人运动学、几何碰撞检测、激光雷达感知、可视化及行为模块。该设计使机器人仿真完全可描述且可复现，通过所提出的IR-SIM智能体技能，可从文本提示生成并修改场景。生成的场景可用于导航算法的自动基准测试以及学习方法训练数据的自动生成。此外，IR-SIM提供了与高保真仿真器及真实世界部署的接口，使用户在原型设计后无需额外编码即可在更逼真场景中验证算法。实验展示了IR-SIM在多任务中的便捷性与通用性：从自然语言构建导航场景、训练避碰策略、对社交导航策略进行基准测试，以及桥接高保真仿真器与真实世界部署。项目网站访问地址：https://github.com/hanruihua/ir-sim。