TableVerse: A Large-scale Tabletop Dataset with Real-world Grounded Layouts for Generalizable Manipulation
Boyuan Wang, Yue Zhang, Xutao Xue, Xueyu Song, Yu Sun
This paper introduces TableVerse, a large-scale dataset of tabletop environments that are realistic, physically consistent, and suitable for training generalizable robotic manipulation policies. Practitioners can use this dataset to improve the performance of robots in tasks like pick-and-place manipulation.
Abstract
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.