Paper page - Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models

Source: https://huggingface.co/papers/2607.05390 Authors:

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Abstract

A large-scale visuotactile dataset called Deform360 is introduced to study deformable object dynamics, enabling comparison between 2D video and 3D particle world models for robotic manipulation tasks.

Predicting object dynamics (i.e.,world modeling) is a fundamental challenge for robotic manipulation, and modelingdeformable objectspresents 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 the2D pixel spaceor more explicit3D 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-scalevisuotactile datasetfeaturing 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 against3D particle models. Finally, we provide a preliminary demonstration indicating the real-world applicability of our dataset by performingrobot planningtasks ondeformable 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-centricworld modeling. Project website: https://deform360.lhy.xyz

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