Dexnet3.0 Pytorch

This repository is implementations of both training and prediction of Grasp Quality CNN (GQ-CNN) with Dexnet3.0 dataset using Pytorch modules.

For more information, please visit original project website and the paper

• Project Website
• Paper: Dex-Net 3.0: Computing Robust Robot Suction Grasp Targets using a New Analytic Model and Deep Learning, Mahler et al., ICRA 2018

This repository features:

• dsets.py - Script for pre-fetching dexnet3.0 dataset onto RAM, split train/validation sets and more.

• model.py - Grasp Quality CNN model consists of torch.nn module.

• training.py - Run this script to train your model. The default options are as below.

  • The number of images: number_of_files x 1000 = 2,760,000 images.
  • Learning rate: 0.001
  • Momemtum: 0.99
  • Epochs : 25
  • Batch size: 64

• predict.py - Prediction with the trained GQ-CNN.

To fetch full dexnet3.0 dataset, there should be at least 20GB free space on your RAM. If you want to train with a fewer images, reduce 'number_of_files' in the training.py

Dataset

• You can download dexnet3.0 dataset in the project website dexnet3.0 dataset

Results

• Using GQ-CNN trained with the Dexnet3.0 dataset, stable suction grasping point on a object can be determined. (Left) Predicted candidates. (Right) Final suction point.