Name: dex-net
Owner: Stanford Vision and Learning Group
Description: Repository for reading the Dex-Net 2.0 HDF5 database of 3D objects, parallel-jaw grasps, and robust grasp metrics
Forked from: BerkeleyAutomation/dex-net
Created: 2017-10-04 23:35:08.0
Updated: 2017-10-04 23:35:10.0
Pushed: 2017-09-25 22:25:15.0
Homepage: https://berkeleyautomation.github.io/dex-net/code.html
Size: 35264
Language: Python
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The dex-net Python package is for opening, reading, and writing HDF5 databases of 3D object models, parallel-jaw grasps, and grasp robustness metrics.
The HDF5 databases can also be used to generate massive datasets associating tuples of point clouds and grasps with binary grasp robustness labels to train Grasp Quality Convolutional Neural Networks (GQ-CNNs) to predict robustness of candidate grasps from point clouds. If you are interested in this functionality, please email Jeff Mahler (jmahler@berkeley.edu) with the subject line: “Interested in GQ-CNN Dataset Generation.”
This package is part of the Dexterity Network (Dex-Net) project. Created and maintained by the AUTOLAB at UC Berkeley.
See the documentation for installation instructions and API Documentation.
The Dex-Net Object Mesh Dataset v1.1 and Dex-Net 2.0 HDF5 database can be downloaded from the data repository.
The code is available under the Apache 2.0 license. If you use this code in a publication, please cite:
Mahler, Jeffrey, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg. “Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics.” Robotics: Science and Systems (2017). Boston, MA.
The repository currently supports our custom ABB YuMi gripper. If you are interested in additional parallel-jaw grippers, please email Jeff Mahler (jmahler@berkeley.edu) with the subject line: “Interested in Contributing to the Dex-Net Grippers” with a description of the parallel-jaw gripper you'd like to add.
The current Dex-Net API does not support the creation of new databases of objects. We plan to release this functionality via an HTTP service in Fall 2017.
If you are interested in using this functionality for research, see the custom-databases branch. However, we cannot provide support at this time.