The project website is https://sites.google.com/view/da2dataset. This repo contains the code for DA2 dataset generation and some scripts that can visualize grasp pairs and render virtual scenes. The paper is available here.
conda create -n DA2 python=3.8
conda activate DA2
git clone https://github.com/ymxlzgy/DA2.git
cd path/to/DA2
mkdir grasp test_tmp
pip install -r requirements.txt
cd path/to/DA2/meshpy
python setup.py develop
Please refer to pytorch official website to find the best version in your case, e.g.,:
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
conda install mayavi -c conda-forge
Download meshes here. After download, unzip them under path/to/DA2 make it like path/to/DA2/simplified.
This is used to individual generation. Need to modify the path OBJ_FILENAME inside posetest.py to your customized path, and run:
cd path/to/DA2/scripts
python posetest.py
The generated grasp pairs will be saved under test_tmp.
Need to modify file_dir to the customized mesh path.
cd path/to/DA2/scripts
python generate_dual_dataset.py
The generated grasp pairs will be stored under grasp.
generate_dual_dataset2.py is used for a parallel generation. If use this script, you need to modify the len(file_list) in generate_dual_dataset.py to a customized number.
To visualize the mesh with accompanying grasp pairs, run:
cd path/to/DA2/scripts
python dual_grasps_visualization.py absolute_path/to/grasp_file --mesh_root path/to/meshes
After download or generating the dataset, run:
cd path/to/DA2/scripts
python render_point_dex.py path/to/dataset
path/to/dataset in our case is path/to/DA2. You may need to change the path under the function load_grasp_path to a customized defined path.
The generated scenes will be under path/to/dataset/table_scene_stand_all. Simulated point clouds will be under path/to/dataset/pc_two_view_all, Virtual camera info will be under path/to/dataset/cam_pose_all.
Scene rendering is time-consuming. To render in parallel, just run multiple scripts in the same time.
If you want to visualize the generated scenes, run:
python render_point_dex.py path/to/dataset --load_existing number_of_the_scene --vis
This work is based on Dex-Net, Acronym, Contact-GraspNet, and diverse-and-stable-grasp.
Many functions under dexnet are from Dex-Net. Didn't remove them in case anyone can notice them and may facilitate one's research.
If you think this repo can help your research, please consider citing:
@article{da2dataset,
author={Zhai, Guangyao and Zheng, Yu and Xu, Ziwei and Kong, Xin and Liu, Yong and Busam, Benjamin and Ren, Yi and Navab, Nassir and Zhang, Zhengyou},
journal={IEEE Robotics and Automation Letters},
title={DA$^2$ Dataset: Toward Dexterity-Aware Dual-Arm Grasping},
year={2022},
volume={7},
number={4},
pages={8941-8948},
doi={10.1109/LRA.2022.3189959}}