TableVerse：一個基於真實世界佈局的大規模桌面資料集，用於可泛化操作

摘要

可泛化機器人操作策略的發展本質上受到大規模、高保真場景資料可用性的限制。雖然近期的自動化合成方法試圖透過文字到佈局生成或簡化程序化生成來填補此缺口，但它們常因物理不合理性以及無法捕捉真實人類環境中複雜密集的雜亂場景而表現不佳。本文提出 TableVerse，一個全自動的真實到模擬流程，將研究典範從想像佈局生成轉向基於非結構化真實世界影像資料的確定性重建。我們的框架能無縫處理未經編排的網路媒體內容，生成具有精確度量尺度、真實拓撲結構與驗證機械穩定性的高保真模擬就緒桌面環境。此外，我們整合了自動任務條件化軌跡生成框架，用於合成高品質、無碰撞的拾取與放置示範。藉由此完整流程，我們建構了 TableVerse-100K 資料集，這是一個包含十萬個獨特且物理一致的環境的大型語料庫，並配對互動式操作軌跡。透過捕捉多樣化的資產組合、真實的空間分佈與高品質示範，TableVerse-100K 建立了高度可擴展且高保真的資料基礎，為未來可泛化機器人操作任務的研究提供了重要價值。

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.