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.
DOFS: A Real-world 3D Deformable Object Dataset with Full Spatial Information for Dynamics Model Learning
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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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Manipulating Elasto-Plastic Objects With 3D Occupancy and Learning-Based Predictive Control
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.