Constrained Generative Sampling of 6-DoF Grasps

This repository provides the code for training VCGS, a position-constrained generative 6-DoF grasp sampler, from the paper Constrained Generative Sampling of 6-DoF Grasps.

Installation

This code has been tested with Python 3.6, PyTorch 1.4, and CUDA 10.0 on Ubuntu 18.04. To install do

1. pip3 install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html

2. Clone this repository: git clone [email protected]:jsll/pytorch_6dof-graspnet.git.

3. Clone pointnet++: [email protected]:erikwijmans/Pointnet2_PyTorch.git.

4. Run cd Pointnet2_PyTorch && pip3 install -r requirements.txt

5. cd pytorch_6dof-graspnet

6. Run pip3 install -r requirements.txt to install the necessary Python libraries.

7. (Optional) Download the trained models either by running sh checkpoints/download_models.sh or manually from here. Trained models are released under CC-BY-NC-SA 2.0.

Download the dataset

You have three options to get the dataset:

1. Run the following script bash dataset/download_dataset.sh
2. Download it by clicking this link.
3. Go here and download it manually.

The default position to place the dataset is in the /dataset/ folder.

Generate a new dataset (Optional)

To generate a new CONG dataset, please use this code.

Training

To train the position-constrained grasp sampler or the evaluator with bare minimum configurations, run:

python3 train.py 

To train the evaluator, run:

python3 train.py 

Citation

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}
}

License

The source code is released under MIT License and the trained weights are released under CC-BY-NC-SA 2.0.