This repository contains a ROS package for running the GG-CNN grasping pipeline on a Kinova Mico arm. For the GG-CNN implementation and training, please see https://github.com/dougsm/ggcnn.

The GG-CNN is a lightweight, fully-convolutional network which predicts the quality and pose of antipodal grasps at every pixel in an input depth image. The lightweight and single-pass generative nature of GG-CNN allows for fast execution and closed-loop control, enabling accurate grasping in dynamic environments where objects are moved during the grasp attempt.

Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach

Douglas Morrison, Peter Corke, Jürgen Leitner

Robotics: Science and Systems (RSS) 2018

arXiv | Video

If you use this work, please cite:

@article{morrison2018closing, 
	title={Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach}, 
	author={Morrison, Douglas and Corke, Peter and Leitner, Jürgen}, 
	booktitle={Robotics: Science and Systems (RSS)}, 
	year={2018} 
}

This code was developed with Python 2.7 on Ubuntu 16.04 with ROS Kinetic. Python requirements can be found in requirements.txt.

You will also require the Kinova ROS Packages and Realsense Camera Packages.

A 3D printed mount for the Intel Realsense SR300 on the Kinova Mico arm can be found in the cad folder.

See https://github.com/dougsm/ggcnn for instructions for downloading or training the GG-CNN model.

This implementation is specific to a Kinova Mico robot and Intel Realsense SR300 camera.

Once the ROS package is compiled and sourced:

1. Lanuch the robot roslaunch kinova_bringup kinova_robot.launch kinova_robotType:=m1n6s200
2. Start the camera roslaunch ggcnn_kinova_grasping wrist_camera.launch
3. Run the GG-CNN node rosrun ggcnn_kinova_grasping run_ggcnn.py
4. To perform open-loop grasping, run rosrun ggcnn_kinova_grasping kinova_open_loop_grasp.py, or to perform closed-loop grasping run rosrun kinova_closed_loop_grasp.py.

Contact

Any questions or comments contact Doug Morrison.