AnyGrasp SDK

AnyGrasp SDK for grasp detection & tracking.

[arXiv] [project] [dataset] [graspnetAPI]

Update

• July 20, 2023 Fix a bug in grasp detection inference code, which may cause no prediction when there are only one or two objects.

Video

AnyGrasp cleaning fragments of a broken pot

AnyGrasp catching swimming robot fish

Requirements

• Python 3.6/3.7/3.8/3.9
• PyTorch 1.7.1 with CUDA 11.0
• MinkowskiEngine v0.5.4

Installation

1. Follow MinkowskiEngine instructions to install Anaconda, cudatoolkit, Pytorch and MinkowskiEngine. Note that you need export MAX_JOBS=2; before pip install if you are running on an laptop due to this issue. If PyTorch reports a compatibility issue during program execution, you can re-install PyTorch via Pip instead of Anaconda.

2. Install other requirements from Pip.

    pip install -r requirements.txt

3. Install pointnet2 module.

    cd pointnet2
    python setup.py install

License Registration

Due to the IP issue, currently we can only release the SDK library file of AnyGrasp in a licensed manner. Please get the feature id of your machine and fill in the form to apply for the license. See license_registration/README.md for details. If you are interested in code implementation, you can refer to our baseline version of network, or a third-party implementation of our GSNet.

Demo Code

Now you can run your code that uses AnyGrasp SDK. See grasp_detection and grasp_tracking for details.

Citation

Please cite these papers in your publications if it helps your research:

@article{fang2023anygrasp,
  title={AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains},
  author = {Fang, Hao-Shu and Wang, Chenxi and Fang, Hongjie and Gou, Minghao and Liu, Jirong and Yan, Hengxu and Liu, Wenhai and Xie, Yichen and Lu, Cewu},
  journal={IEEE Transactions on Robotics (T-RO)},
  year={2023}
}

@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},
  pages={11444--11453},
  year={2020}
}

@inproceedings{wang2021graspness,
  title={Graspness discovery in clutters for fast and accurate grasp detection},
  author={Wang, Chenxi and Fang, Hao-Shu and Gou, Minghao and Fang, Hongjie and Gao, Jin and Lu, Cewu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={15964--15973},
  year={2021}
}