Note: Python 2.x support has officially been dropped.

Berkeley AUTOLAB's GQCNN Package

Package Overview

The gqcnn Python package is for training and analysis of Grasp Quality Convolutional Neural Networks (GQ-CNNs). It is part of the ongoing Dexterity-Network (Dex-Net) project created and maintained by the AUTOLAB at UC Berkeley.

Installation and Usage

Please see the docs for installation and usage instructions.

Citation

If you use any part of this code in a publication, please cite the appropriate Dex-Net publication.

Guide for RISE members

(WARNING) Download pre-trained model from our synology. Please see details on bellow information.

Installation

Download code on your catkin_ws.

git clone -b melodic-devel --single-branch https://github.com/rise-lab-skku/rise-gqcnn.git

Recommanded: Use virtual environment and activate it.

cd rise-gqcnn
virtualenv -p python3.6 --system-site-packages venv
source venv/bin/activate

Change directories into the gqcnn repository and run the pip installation.

pip install .

Download pre-trained models from our synology

(WARNING) Official download link is broken. Please follow the bellow intruction.

Create directory in the gqcnn repository.

mkdir -p models

Download pre-trained models on models directory. The models can be found on our synology /Research Projects/2020_지능증강/NN_models/official-gqcnn-models.

Unzip pre-trained models.

cd models
unzip -a GQCNN-4.0-PJ.zip
unzip -a GQCNN-4.0-SUCTION.zip
unzip -a FC-GQCNN-4.0-PJ.zip
unzip -a FC-GQCNN-4.0-SUCTION.zip
cd ..

Usage

Start the grasp planning service:

roslaunch gqcnn grasp_planning_service.launch ns:=pj_gqcnn model_name:=FC-GQCNN-4.0-PJ fully_conv:=true

The example ROS policy can then be queried on saved images using:

python examples/policy_ros.py --depth_image data/examples/clutter/phoxi/fcgqcnn/depth_0.npy --segmask data/examples/clutter/phoxi/fcgqcnn/segmask_0.png --camera_intr data/calib/phoxi/phoxi.intr --namespace pj_gqcnn