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

K. W. Samuel Au; Xiangyu Chu; Yunxi Tang; Zhen Zhang

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

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arxiv 2410.21758 v1 pith:C5O6EK35 submitted 2024-10-29 cs.CV cs.RO

Zhen Zhang , Xiangyu Chu , Yunxi Tang , K. W. Samuel Au This is my paper · ORCID

classification cs.CVcs.RO

keywords datasetinformationactioncollectiondatadeformabledofsdynamics

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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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Manipulating Elasto-Plastic Objects With 3D Occupancy and Learning-Based Predictive Control
cs.RO 2025-05 conditional novelty 6.0 of 10

3D occupancy representation with a learned 3D CNN-GNN dynamics model and MPC enables a robot to shape plasticine into letter goals in both simulation and the real world.

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