TableVerse：面向通用操作的大规模桌面数据集与真实世界布局

摘要

通用型机器人操作策略的发展本质上受到大规模、高保真场景数据可用性的限制。尽管近期自动化合成方法尝试通过文本到布局的想象生成或简化程序化生成来弥合这一差距，但这些方法常因物理不可行性而失败，且无法捕捉真实人类环境中复杂密集的杂乱状态。本文提出TableVerse——一种全自动化Real2Sim流水线，将范式从想象性布局生成转向基于非结构化野外图像数据的确定性重建。该框架能够无缝处理非脚本化的互联网媒体，生成具备精确度量尺度、真实拓扑结构及经过验证的机械稳定性的高保真、可仿真桌面环境。此外，我们集成自动化任务条件轨迹生成框架，用于合成高质量、无碰撞的抓取与放置演示。依托完整流水线，我们构建了TableVerse-100K数据集——包含10万个独特、物理一致环境及其配套交互操作轨迹的大规模语料库。通过涵盖多样化的资产组合、真实的空间分布及高质量演示，TableVerse-100K建立了高度可扩展且高保真的数据基础，为推进通用型机器人操作任务的未来研究提供了重要价值。

English

The development of generalizable robotic manipulation policies is inherently bounded by the availability of large-scale, high-fidelity scene data. While recent automated synthesis methods attempt to bridge this gap via text-to-layout hallucination or simplified procedural generation, they frequently suffer from physical implausibility and fail to capture the complex, dense clutter of actual human environments. In this paper, we introduce TableVerse, a fully automated Real2Sim pipeline that shifts the paradigm from imaginative layout generation to deterministic reconstruction from unstructured, in-the-wild image data. Our framework seamlessly processes unscripted internet media into high-fidelity, simulation-ready tabletop environments with accurate metric scales, authentic topologies, and verified mechanical stability. Furthermore, an automated task-conditioned trajectory generation framework is integrated to synthesize high-quality, collision-free pick-and-place demonstrations. Leveraging this complete pipeline, we construct the TableVerse-100K Dataset, a large-scale corpus comprising 100,000 unique, physically consistent environments paired with interactive manipulation trajectories. By capturing diverse asset compositions, realistic spatial distributions, and high-quality demonstrations, TableVerse-100K establishes a highly scalable and high-fidelity data foundation, providing significant value to facilitate future research in generalizable robotic manipulation tasks.