DOFS: A Real-world 3D Deformable Object Dataset with Full Spatial Information for Dynamics Model Learning

Zhen Zhang, Xiangyu Chu, TANG Yunxi, K. W. Samuel Au

Keywords: Deformable Object Manipulation, Data Collection, State Representation

TL;DR: 5 Pages; 6 figures

Abstract: This work proposes DOFS, a pilot dataset of 3D deformable objects (DOs) (e.g., elasto-plastic objects) with full spatial information (i.e., top, side, and bottom information) using a novel and low-cost data collection platform with a transparent operating plane. The dataset consists of active manipulation action, multi-view RGB-D images, well-registered point clouds, 3D deformed mesh, and 3D occupancy with semantics, using a pinching strategy with a two-parallel-finger gripper. In addition, we trained a neural network with the down-sampled 3D occupancy and action as input to model the dynamics of an elasto-plastic object. Our dataset and all CADs of the data collection system will be released soon on our website.

Submission Number: 38