Deform360：一個大規模多視角視觸覺資料集，用於可變形世界模型

摘要

预测物体动态（即世界模型）是机器人操作领域的基础性挑战，而可变形物体因其高维状态空间和复杂材料特性，尤为棘手。当前世界模型主要通过两种范式解决这一问题：在二维像素空间或更明确的三维几何空间上学习动力学。然而，由于缺乏多样化、大规模的真实世界数据，对这两种范式相对优势与局限的系统性理解仍不明确。为应对这一挑战，我们提出Deform360——一个大规模视触觉数据集，涵盖198个日常物体、1980个交互序列，以及由41个环绕视角相机和双手触觉夹爪采集的超过215小时观测数据，以捕捉全局运动及接触诱发的局部形变。借助创新的无标记视触觉3D追踪流程提取密集几何与运动信息，我们系统评估了当前最先进的世界模型，对比了2D视频模型与3D粒子模型。最后，我们通过执行可变形物体上的机器人规划任务，初步展示了该数据集在真实世界中的适用性。我们的分析揭示了结构先验与可扩展性之间权衡的关键洞见，为未来通用可变形物体世界模型的研究提供了坚实基准。项目网站：https://deform360.lhy.xyz

English

Predicting object dynamics (i.e., world modeling) is a fundamental challenge for robotic manipulation, and modeling deformable objects presents a particularly difficult case due to their high-dimensional state spaces and complex material properties. While current world models approach this through two distinct paradigms: learning the dynamics over the 2D pixel space or more explicit 3D geometric space. A systematic understanding of their relative strengths and limitations remains elusive due to the lack of diverse, large-scale real-world data. To address this, we present Deform360, a large-scale visuotactile dataset featuring 198 daily-life objects, 1,980 interaction sequences, and over 215 hours of observations from 41 surround-view cameras and bimanual tactile grippers to capture both global motion and contact-induced local deformations. Leveraging a novel markerless visuotactile 3D tracking pipeline to extract dense geometry and motion, we systematically evaluate current state-of-the-art world models, comparing 2D video models against 3D particle models. Finally, we provide a preliminary demonstration indicating the real-world applicability of our dataset by performing robot planning tasks on deformable objects. Our analysis reveals key insights into the trade-offs between structural priors and scalability, providing a solid benchmark for future research in generalizable deformable object-centric world modeling. Project website: https://deform360.lhy.xyz