Abstract
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.
Abstract (translated)
现实中的机器人任务通常涉及较长的时间跨度，并且往往横跨多层楼，需要复杂的空间推理能力。然而，现有的嵌入式基准测试大多局限于单层室内环境，无法反映实际任务的复杂性。为此，我们引入了MANSION，这是一个首屈一指的语言驱动框架，用于生成建筑规模、多楼层的3D环境。考虑到垂直结构约束，MANSION能够生成现实主义强且可导航的整体建筑结构，并包含多样化的人类友好型场景，从而支持跨楼层长时任务的发展和评估。 基于此框架，我们发布了MansionWorld数据集，其中包含了超过1000个多样化的建筑物模型，从医院到办公室应有尽有。此外，我们还提供了一个任务语义场景编辑代理，它能够通过开放式词汇命令自定义这些环境以满足特定用户需求。基准测试表明，在我们的设置中，最先进的代理性能明显下降，这证明MANSION已成为下一代空间推理和规划技术的重要测试平台。
URL
https://arxiv.org/abs/2603.11554
