Published June 23, 2022 | Version 1.0

VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction

  • 1. Maynooth University, Ireland

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)

Name Size
md5:211c6b2e39e71f2fce25b6f4e504b190
7.5 GB Preview Download
md5:1739438af7a98ccdfd8753df1a2cb0b6
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md5:8cb7a8de86d8b6cfc8d750c513303b14
4.9 GB Preview Download
md5:74f58747ebb0cb7452b4db90e7ee0187
7.3 GB Preview Download
md5:633c6a36f5de977979a23c14bfc27f8f
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.