VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction
Description
This dataset includes all the data and trained models to replicate our work for VGQ-CNN (accepted for IJCNN 2022). You can find the code to use this dataset on github. To replicate the work done for VGQ-CNN, use the data in vgq-dset.zip. Trained models of VGQ-CNN, Fast-VGQ-CNN and GQ-CNN are available in VGQ-CNN_models.zip.
To create your own, subsampled training and testing data, adjust our code on github to your subsampling constraints and use full_rendered_dset (created by unpacking full_rendered_dset_tensors.zip and full_rendered_dset_images.zip into the unpacked directory of full_rendered_dset_info.zip).
Files
full_rendered_dset_images.zip
Files
(20.4 GB)
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md5:633c6a36f5de977979a23c14bfc27f8f
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697.9 MB | Preview Download |
Additional details
References
- A. Konrad, J. McDonald and R. Villing, "VGQ-CNN: Moving beyond fixed cameras and top-grasps for grasp quality prediction," to appear in International Joint Conference on Neural Networks (IJCNN), 2022.
- J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg, "Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics," in Robotics: Science and Systems (RSS), 2017.