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.
Please see the docs for installation and usage instructions.
1.clone docker to your <workspace_ws>.
git clone https://github.com/errrr0501/docker_20.04_CUDA12_tf1.15
2.build it and run it.
3.clone and build realsense_ros2_wrapper.
#open a new terminal
mkdir <your realsense workspace>
cd <your realsense workspace>
git clone https://github.com/IntelRealSense/realsense-ros.git -b ros2-development
colcon build
4.make a workspace and clone gqcnn and autolab_core.
#open a new terminal
mkdir <your gqcnn workspace>
cd <your gqcnn workspace>
git clone https://github.com/errrr0501/ROS2_gqcnn.git
git clone https://github.com/errrr0501/ROS2_autolab_core.git
colcon build
5.use with your camera topic.
#open a new terminal
cd <your realsense workspace>
source install/setup.bash
ros2 launch realsense2_camera rs_launch.py depth_module.profile:=640x480x30 rgb_camera.profile:=640x480x30 align_depth.enable:=true
#open a new terminal
cd <your gqcnn workspace>
source install/setup.bash
ros2 launch gqcnn grasp_planning_service.launch.py
#open a new terminal
source install/setup.bash
python3 src/ROS2_gqcnn/gqcnn/examples/policy_camera_ros2.py
If you use any part of this code in a publication, please cite the appropriate Dex-Net publication.