This repository provides the code for creating the CONG dataset from the paper Constrained Generative Sampling of 6-DoF Grasps.

Create the conda environment:

conda env create -f environment.yml

Run the script using the command python main.py with the following options:

• -mp, --mesh_path: Path to meshes (default: data/shapenetsem_example_meshes/)
• -gp, --grasp_path: Path to acronym grasps (default: data/acronym_example_grasps/)
• -sp, --folder_for_storing: Path where to save results (default: /tmp/constrained_grasping_dataset/)
• -np, --number_of_points_to_sample_on_mesh: Number of points to sample on the mesh (default: 1024)
• -th, --threshold: Threshold between center of grasps and query points (default: 0.002)
• -nq, --num_query_points: Number of query points to sample from the mesh (default: 50)
• -v, --visualize: Flag to visualize the results

Example:

python main.py -v

1. Download the full acronym dataset: acronym.tar.gz
2. Download the ShapeNetSem meshes from https://www.shapenet.org/

python main.py -mp path/to/ShapeNetSem/meshes -gp path/to/Acronym/dataset

Feel free to open issues, suggest enhancements, or make pull requests.

If this code is useful in your research, please consider citing:

@article{lundell2023constrained,
  title={Constrained generative sampling of 6-dof grasps},
  author={Lundell, Jens and Verdoja, Francesco and Le, Tran Nguyen and Mousavian, Arsalan and Fox, Dieter and Kyrki, Ville},
  journal={arXiv preprint arXiv:2302.10745},
  year={2023}
}

MIT License