Prerequisites¶

Python¶

The gqcnn package has only been tested with Python 3.5, Python 3.6, and Python 3.7.

Ubuntu¶

The gqcnn package has only been tested with Ubuntu 12.04, Ubuntu 14.04 and Ubuntu 16.04.

Virtualenv¶

We highly recommend using a Python environment management system, in particular Virtualenv, with the Pip and ROS installations. Note: Several users have encountered problems with dependencies when using Conda.

Pip Installation¶

The pip installation is intended for users who are only interested in 1) Training GQ-CNNs or 2) Grasp planning on saved RGBD images, not interfacing with a physical robot. If you have intentions of using GQ-CNNs for grasp planning on a physical robot, we suggest you install as a ROS package.

1. Clone the repository¶

Clone or download the project from Github.

$ git clone https://github.com/BerkeleyAutomation/gqcnn.git

2. Run pip installation¶

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

$ pip install .

This will install gqcnn in your current virtual environment.

ROS Installation¶

Installation as a ROS package is intended for users who wish to use GQ-CNNs to plan grasps on a physical robot.

1. Clone the repository¶

Clone or download the project from Github.

$ cd <PATH_TO_YOUR_CATKIN_WORKSPACE>/src
$ git clone https://github.com/BerkeleyAutomation/gqcnn.git

2. Build the catkin package¶

Build the catkin package.

$ cd <PATH_TO_YOUR_CATKIN_WORKSPACE>
$ catkin_make

Then re-source devel/setup.bash for the package to be available through Python.

Docker Installation¶

We currently do not provide pre-built Docker images, but you can build them yourself. This will require you to have installed Docker or Nvidia-Docker if you plan on using GPUs. Note that our provided build for GPUs uses CUDA 10.0 and cuDNN 7.0, so make sure that this is compatible with your GPU hardware. If you wish to use a different CUDA/cuDNN version, change the base image in docker/gpu/Dockerfile to the desired CUDA/cuDNN image distribution. Note that other images have not yet been tested.

1. Clone the repository¶

Clone or download the project from Github.

$ git clone https://github.com/BerkeleyAutomation/gqcnn.git

2. Build Docker images¶

Change directories into the gqcnn repository and run the build script.

$ ./scripts/docker/build-docker.sh

This will build the images gqcnn/cpu and gqcnn/gpu.

3. Run Docker image¶

To run gqcnn/cpu:

$ docker run --rm -it gqcnn/cpu

To run gqcnn/gpu:

$ nvidia-docker run --rm -it gqcnn/gpu

Note the use of nvidia-docker in the latter to enable the Nvidia runtime.

You will then see an interactive shell like this:

$ root@a96488604093:~/Workspace/gqcnn#

Now you can proceed to run the examples and tutorial!