To run inference (main_test) you need to check the input size. It must match the input size that the network was trained on.
Currently all main_ have a default of 750 input points. The main_test.py always gives the same result (which is not a good sign)
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PointNetGPD (ICRA 2019, arXiv) is an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud.
PointNetGPD is light-weighted and can directly process the 3D point cloud that locates within the gripper for grasp evaluation. Taking the raw point cloud as input, our proposed grasp evaluation network can capture the complex geometric structure of the contact area between the gripper and the object even if the point cloud is very sparse.
To further improve our proposed model, we generate a larger-scale grasp dataset with 350k real point cloud and grasps with the YCB objects Dataset for training.
- All the code should be installed in the following directory:
mkdir -p $HOME/code/
cd $HOME/code/
- Set environment variable
PointNetGPD_FOLDER
in your$HOME/.bashrc
file.
export PointNetGPD_FOLDER=$HOME/code/PointNetGPD
-
Install
pcl-tools
viasudo apt install pcl-tools
. -
An example for create a virtual environment:
conda create -n pointnetgpd python=3.10 numpy ipython matplotlib opencv mayavi -c conda-forge
-
Make sure in your Python environment do not have same package named
meshpy
ordexnet
. -
Install PyTorch: https://pytorch.org/get-started/locally/
-
Clone this repository:
cd $HOME/code git clone https://github.com/lianghongzhuo/PointNetGPD.git
-
Install our requirements in
requirements.txt
cd $PointNetGPD_FOLDER pip install -r requirements.txt
-
Install our modified meshpy (Modify from Berkeley Automation Lab: meshpy)
cd $PointNetGPD_FOLDER/meshpy python setup.py develop
-
Install our modified dex-net (Modify from Berkeley Automation Lab: dex-net)
cd $PointNetGPD_FOLDER/dex-net python setup.py develop
-
Modify the gripper configurations to your own gripper
vim $PointNetGPD_FOLDER/dex-net/data/grippers/robotiq_85/params.json
These parameters are used for dataset generation:
"min_width": "force_limit": "max_width": "finger_radius": "max_depth":
These parameters are used for grasp pose generation at experiment:
"finger_width": "real_finger_width": "hand_height": "hand_height_two_finger_side": "hand_outer_diameter": "hand_depth": "real_hand_depth": "init_bite":
- You can download the dataset from: https://tams.informatik.uni-hamburg.de/research/datasets/PointNetGPD_grasps_dataset.zip
- After download, extract the zip file and rename it to
ycb_grasp
- Move the folder to
$PointNetGPD_FOLDER/PointNetGPD/data/
- Download YCB object set from YCB Dataset.
A command line tool for download ycb dataset can be found at: ycb-tools.
cd $PointNetGPD_FOLDER/data git clone https://github.com/lianghongzhuo/ycb-tools cd ycb-tools python download_ycb_dataset.py rgbd_512
- Manage your dataset at:
$PointNetGPD_FOLDER/PointNetGPD/data
Every object should have a folder, structure like this:├002_master_chef_can |└── google_512k | ├── nontextured.obj (generated by pcl-tools) | ├── nontextured.ply | ├── nontextured.sdf (generated by SDFGen) |└── rgbd | ├── *.jpg | ├── *.h5 | ├── ... ├003_cracker_box └004_sugar_box ...
- Install SDFGen from GitHub:
cd $PointNetGPD_FOLDER git clone https://github.com/jeffmahler/SDFGen.git cd SDFGen && mkdir build && cd build && cmake .. && make
- Install Open3D
pip install open3d
- Generate
nontextured.sdf
file andnontextured.obj
file usingpcl-tools
andSDFGen
by running:cd $PointNetGPD_FOLDER/dex-net/apps python read_file_sdf.py
- Generate dataset by running the code:
where
cd $PointNetGPD_FOLDER/dex-net/apps python generate-dataset-canny.py [prefix]
[prefix]
is optional, it will add a prefix on the generated files.
-
Visualization grasps
cd $PointNetGPD_FOLDER/dex-net/apps python read_grasps_from_file.py
Note:
- This file will visualize the grasps in
$PointNetGPD_FOLDER/PointNetGPD/data/ycb_grasp/
folder
- This file will visualize the grasps in
-
Visualization object normal
cd $PointNetGPD_FOLDER/dex-net/apps python Cal_norm.py
This code will check the norm calculated by meshpy
and pcl
library.
-
Data prepare:
cd $PointNetGPD_FOLDER/PointNetGPD/data
Make sure you have the following files, The links to the dataset directory should add by yourself:
├── google2cloud.csv (Transform from google_ycb model to ycb_rgbd model) ├── google2cloud.pkl (Transform from google_ycb model to ycb_rgbd model) └── ycb_grasp (generated grasps)
Generate point cloud from RGB-D image, you may change the number of process running in parallel if you use a shared host with others
cd $PointNetGPD_FOLDER/PointNetGPD python ycb_cloud_generate.py
Note: Estimated running time at our
Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz
dual CPU with 56 Threads is 36 hours. Please also remove objects beyond the capacity of the gripper. -
Run the experiments:
cd $PointNetGPD_FOLDER/PointNetGPD
Launch a tensorboard for monitoring
tensorboard --log-dir ./assets/log --port 8080
and run an experiment for 200 epoch
python main_1v.py --epoch 200 --mode train --batch-size x (x>=16)
File name and corresponding experiment:
main_1v.py --- 1-viewed point cloud, 2 class main_1v_mc.py --- 1-viewed point cloud, 3 class main_1v_gpd.py --- 1-viewed point cloud, GPD main_fullv.py --- Full point cloud, 2 class main_fullv_mc.py --- Full point cloud, 3 class main_fullv_gpd.py --- Full point cloud, GPD
For GPD experiments, you may change the input channel number by modifying
input_chann
in the experiment scripts(only 3 and 12 channels are available)
-
Get UR5 robot state:
Goal of this step is to publish a ROS parameter tell the environment whether the UR5 robot is at home position or not.
cd $PointNetGPD_FOLDER/dex-net/apps python get_ur5_robot_state.py
-
Run perception code: This code will take depth camera ROS info as input, and gives a set of good grasp candidates as output. All the input, output messages are using ROS messages.
cd $PointNetGPD_FOLDER/dex-net/apps python kinect2grasp.py
arguments: -h, --help show this help message and exit --cuda using cuda for get the network result --gpu GPU set GPU number --load-model LOAD_MODEL set witch model you want to use (rewrite by model_type, do not use this arg) --show_final_grasp show final grasp using mayavi, only for debug, not working on multi processing --tray_grasp not finished grasp type --using_mp using multi processing to sample grasps --model_type MODEL_TYPE selet a model type from 3 existing models
If you found PointNetGPD useful in your research, please consider citing:
@inproceedings{liang2019pointnetgpd,
title={{PointNetGPD}: Detecting Grasp Configurations from Point Sets},
author={Liang, Hongzhuo and Ma, Xiaojian and Li, Shuang and G{\"o}rner, Michael and Tang, Song and Fang, Bin and Sun, Fuchun and Zhang, Jianwei},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2019}
}