Berkeley AUTOLAB's GQCNN Package
Documentation: https://berkeleyautomation.github.io/gqcnn
Overview
The gqcnn Python package is for training and analysis of Grasp Quality Convolutional Neural Networks (GQ-CNNs).
This package is part of the Dexterity Network (Dex-Net) project: https://berkeleyautomation.github.io/dex-net
Created and maintained by the AUTOLAB at UC Berkeley: https://autolab.berkeley.edu
Installation
See the website at https://berkeleyautomation.github.io/gqcnn for installation instructions and API Documentation.
Datasets
Our GQ-CNN training datasets and trained models can be downloaded from this link.
ROS Service
We developed a ROS service for grasp planning with GQ-CNNs. The service takes as input a color image, depth image, camera info topic, and bounding box for the object in image space, and returns a parallel-jaw gripper pose relative to the camer along with a predicted probability of success. This has been tested on our setup with ROS Jade on Ubuntu 14.04
To illustrate using our ROS service, we've shared the ROS node that we use to plan grasps for and control an ABB YuMi on our local setup. This file should be considered READ-ONLY as it uses parameters specific to our setup. If you have interest in replicating this functionality on your own robot, please contact Jeff Mahler (jmahler@berkeley.edu) with the subject line: "Interested in GQ-CNN ROS Service".