Building robust, intelligent robots requires testing them in complex environments. However, gathering data in the physical world is expensive, slow, and often dangerous. It is nearly impossible to safely train for real-world critical risks, such as high-speed collisions or hardware failures. Worse, real-world data is usually biased toward “normal” conditions, leaving robots unprepared for the unexpected.​ Simulation is essential to bridge this gap, providing a risk-free environment for rigorous development. However, traditional pipelines struggle to support the complex needs of modern robotics. Today’s generalist robots must master multimodal learning—fusing diverse inputs such as vision, touch, and proprioception to navigate messy, unstructured worlds. This creates a new requirement for simulation: it must deliver scale, realism, and multimodal sensing all in one tight training loop, something traditional CPU-bound simulators cannot handle efficiently.…