Most existing robot simulators prioritize rigid-body dynamics and photorealistic rendering, but largely neglect the thermally and optically complex phenomena that characterize real-world fire environments. For robots envisioned as future firefighters, this limitation hinders both reliable capability evaluation and the generation of representative training data prior to deployment in hazardous scenarios. To address these challenges, we introduce Fire as a Service (FaaS), a novel, asynchronous co-simulation framework that augments existing robot simulators with high-fidelity and computationally efficient fire simulations. Our pipeline enables robots to experience accurate, multi-species thermodynamic heat transfer and visually consistent volumetric smoke without disrupting high-frequency rigid-body control loops. We demonstrate that our framework can be integrated with diverse robot simulators to generate physically accurate fire behavior, benchmark thermal hazards encountered by robotic platforms, and collect realistic multimodal perceptual data. Crucially, its real-time performance supports human-in-the-loop teleoperation, enabling the successful training of reactive, multimodal policies via Behavioral Cloning. By adding fire dynamics to robot simulations, FaaS provides a scalable pathway toward safer, more reliable deployment of robots in fire scenarios.

翻译：现有机器人模拟器大多优先考虑刚体动力学与逼真渲染，但严重忽略了表征真实火场环境的热学与光学复杂现象。对于被设想为未来消防员的机器人而言，这一限制阻碍了其在危险场景部署前进行可靠能力评估及生成代表性训练数据。为解决这些挑战，我们提出"火作为服务"（Fire as a Service，FaaS）——一种新颖的异步协同模拟框架，可为现有机器人模拟器注入高保真且计算高效的火场模拟。我们的流水线使机器人能够体验精确的多物种热力学传热与视觉一致的体积烟雾，同时不干扰高频刚体控制回路。我们证明该框架可集成至多种机器人模拟器，以生成物理精确的火势行为、评估机器人平台遭遇的热危害，并收集逼真的多模态感知数据。至关重要的是，其实时性能支持人在回路遥操作，从而通过行为克隆成功训练响应式多模态策略。通过为机器人模拟添加火场动力学，FaaS为机器人在火场场景中更安全、更可靠的部署提供了可扩展路径。