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

• URL: http://arxiv.org/abs/2410.21758v1
• Date: Tue, 29 Oct 2024 05:46:16 GMT
• Title: DOFS: A Real-world 3D Deformable Object Dataset with Full Spatial Information for Dynamics Model Learning
• Authors: Zhen Zhang, Xiangyu Chu, Yunxi Tang, K. W. Samuel Au,
• Abstract summary: This work proposes DOFS, a pilot dataset of 3D deformable objects (DOs) (e.g., elasto-plastic objects) with full spatial information. The dataset consists of active manipulation action, multi-view RGB-D images, well-registered point clouds, 3D deformed mesh, and 3D occupancy with semantics. 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.
• Score: 7.513355021861478
• License:
• 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.

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