Abstract
Real-world data collection for embodied agents remains costly and unsafe, calling for scalable, realistic, and simulator-ready 3D environments. However, existing scene-generation systems often rely on rule-based or task-specific pipelines, yielding artifacts and physically invalid scenes. We present SAGE, an agentic framework that, given a user-specified embodied task (e.g., "pick up a bowl and place it on the table"), understands the intent and automatically generates simulation-ready environments at scale. The agent couples multiple generators for layout and object composition with critics that evaluate semantic plausibility, visual realism, and physical stability. Through iterative reasoning and adaptive tool selection, it self-refines the scenes until meeting user intent and physical validity. The resulting environments are realistic, diverse, and directly deployable in modern simulators for policy training. Policies trained purely on this data exhibit clear scaling trends and generalize to unseen objects and layouts, demonstrating the promise of simulation-driven scaling for embodied AI. Code, demos, and the SAGE-10k dataset can be found on the project page here: this https URL.
Abstract (translated)
真实世界中为具身智能体（如机器人或虚拟角色）收集数据既昂贵又不安全，因此需要可扩展、逼真且适用于模拟器的3D环境。然而，现有的场景生成系统通常依赖于基于规则的方法或特定任务的管道，这会导致生成的效果不佳和物理上无效的场景。我们介绍了SAGE框架，这是一种智能体驱动的方法，能够根据用户指定的任务（例如，“拿起碗并把它放在桌子上”），理解意图，并自动生成大规模的、可用于模拟器的环境。 该方法集成了多个布局与物体组合的生成器以及评估语义合理性、视觉真实性和物理稳定性的批评者。通过迭代推理和适应性工具选择，智能体能够不断自我优化场景，直至符合用户意图并满足物理有效性要求。最终产生的环境既逼真又多样化，并且可以直接部署在现代模拟器中用于策略训练。 仅使用这种数据进行的策略训练显示出明确的规模效应，并能推广到未见过的对象和布局上，这表明了基于模拟的数据驱动扩展对具身AI的前景。该项目的相关代码、演示以及SAGE-10k数据集可以在项目页面（此链接）找到。
URL
https://arxiv.org/abs/2602.10116
