首个支持多楼层的文本生成3D建筑框架，助力机器人长时跨层任务研究。
MANSION: Multi-floor lANguage-to-3D Scene generatIOn for loNg-horizon tasks
- 基于语言指令生成符合垂直结构约束的多楼层3D场景
- 构建超1000栋多样建筑的数据集MansionWorld，涵盖医院、办公室等
- 适合研究长时序空间推理与规划的机器人系统开发者
真实世界的机器人任务通常具有长时间跨度且跨越多个楼层，需要强大的空间推理能力。然而，现有具身智能基准测试大多局限于单层室内环境，难以反映现实任务的复杂性。我们提出MANSION，首个由语言驱动的建筑级多楼层3D环境生成框架。该框架考虑垂直结构约束，可生成真实、可导航的整栋建筑结构及多样化的人类友好场景，支持跨楼层长时任务的研发与评估。基于此，我们发布MansionWorld数据集，包含超过1000栋从医院到办公室的多样化建筑，并配套任务语义场景编辑代理，可通过开放词汇命令定制环境以满足特定需求。基准测试显示，当前先进智能体在本设置下性能显著下降，确立了MANSION作为下一代空间推理与规划技术的关键测试平台。
原文摘要 · Abstract (English)
Real-world robotic tasks are long-horizon and often span multiple floors, demanding rich spatial reasoning. However, existing embodied benchmarks are largely confined to single-floor in-house environments, failing to reflect the complexity of real-world tasks. We introduce MANSION, the first language-driven framework for generating building-scale, multi-floor 3D environments. Being aware of vertical structural constraints, MANSION generates realistic, navigable whole-building structures with diverse, human-friendly scenes, enabling the development and evaluation of cross-floor long-horizon tasks. Building on this framework, we release MansionWorld, a dataset of over 1,000 diverse buildings ranging from hospitals to offices, alongside a Task-Semantic Scene Editing Agent that customizes these environments using open-vocabulary commands to meet specific user needs. Benchmarking reveals that state-of-the-art agents degrade sharply in our settings, establishing MANSION as a critical testbed for the next generation of spatial reasoning and planning.
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