GraspNet and Pointnet2 PyTorch Upgrade (v1.13.1)

Our main contribution is upgrading GraspNet and Pointnet2_PyTorch to the latest PyTorch version (v1.13.1). Currently, there isn't a comprehensive implementation available, and most of the work involves patching various bugs. This version consolidates all the necessary fixes and improvements, providing a stable and reliable implementation. You can directly use this upgraded version to replace the original GraspNet and Pointnet2_PyTorch implementations.

Environment Requirements

• Ubuntu 23.04
• Torch 1.13.1
• Torchvision 0.14.1
• CUDA 11.8

Table of Contents

• Introduction
• Installation
• Usage
• Citation
• References
• Contributing
• License
• Acknowledgments

Introduction

GraspNet is designed to facilitate the generation of reliable grasp poses for robotic systems. This implementation, based on PyTorch v1.13.1, leverages advanced techniques to improve the success rate and adaptability of robotic grasping in cluttered and dynamic environments. Additionally, this repository includes an upgraded version of Pointnet2_PyTorch, also compatible with PyTorch v1.13.1.

Installation

To install the necessary modules, please follow these steps:

git clone https://github.com/H-Freax/GraspNet_Pointnet2_PyTorch1.13.1.git
cd GraspNet_Pointnet2_PyTorch1.13.1

Additionally, you need to install the pointnet2 and knn modules:

cd graspnet/pointnet2
python setup.py install

cd ../knn
python setup.py install

Potential Issues of Installation

When installing graspnetAPI, you might encounter the following problem:

× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [18 lines of output]
The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
rather than 'sklearn' for pip commands.

Solution:

export SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True

Check the compatible version of torch and torchvision of your machine (especially the CUDA version) if the following problem occurs:

RuntimeError: CUDA error: no kernel image is available for execution on the device

Solution: Install torch with the right CUDA version, e.g.

# CUDA 11.8
pip3 install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117

If you still face errors, please try the following:

sudo apt-get install python3-dev
conda install gxx_linux-64
conda install gcc_linux-64

Change float to float64 if necessary.

Install CUDA 11.8:

1. Download the CUDA 11.8 installer from NVIDIA.
2. Run the installer:

   sudo bash cuda_11.8.0_520.61.05_linux.run

Add the following lines to your ~/.bashrc:

export CUDA_HOME=/usr/local/cuda-11.8
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
export PATH=$PATH:/usr/local/cuda-11.8/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.8/lib64
export CUDADIR=/usr/local/cuda-11.8

Usage

To use this upgraded version of GraspNet and Pointnet2_PyTorch, simply replace the original implementations with this repository and follow the usual steps for data preparation and inference.

Citation

Please cite our paper in your publications if it helps your research:

@software{GraspNet_PyTorch_2024,
  author = {Yaoyao Qian},
  title = {GraspNet PyTorch v1.13.1},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/H-Freax/GraspNet_Pointnet2_PyTorch1.13.1}},
}

References

This work builds on and refers to the following projects:

• Vision-Language-Grasping
• GraspNet-baseline
• Pointnet2_PyTorch

Additional References

@article{pytorchpointnet++,
  Author = {Erik Wijmans},
  Title = {Pointnet++ Pytorch},
  Journal = {https://github.com/erikwijmans/Pointnet2_PyTorch},
  Year = {2018}
}

@inproceedings{qi2017pointnet++,
  title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
  author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
  booktitle={Advances in Neural Information Processing Systems},
  pages={5099--5108},
  year={2017}
}

@article{fang2023robust,
  title={Robust grasping across diverse sensor qualities: The GraspNet-1Billion dataset},
  author={Fang, Hao-Shu and Gou, Minghao and Wang, Chenxi and Lu, Cewu},
  journal={The International Journal of Robotics Research},
  year={2023},
  publisher={SAGE Publications Sage UK: London, England}
}

@inproceedings{fang2020graspnet,
  title={GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping},
  author={Fang, Hao-Shu and Wang, Chenxi and Gou, Minghao and Lu, Cewu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={11444--11453},
  year={2020}
}

@INPROCEEDINGS{10161041,
  author={Xu, Kechun and Zhao, Shuqi and Zhou, Zhongxiang and Li, Zizhang and Pi, Huaijin and Zhu, Yifeng and Wang, Yue and Xiong, Rong},
  booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={A Joint Modeling of Vision-Language-Action for Target-oriented Grasping in Clutter}, 
  year={2023},
  pages={11597-11604},
  doi={10.1109/ICRA48891.2023.10161041}
}

Contributing

We welcome contributions to improve GraspNet. If you are interested in contributing, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

We would like to thank all contributors and the PyTorch community for their continuous support.