GitHub - graspnet/anygrasp_sdk (original) (raw)

AnyGrasp SDK

AnyGrasp SDK for grasp detection & tracking.

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

Update

• June 10, 2026 We are testing a new license tool, where lib_cxx.so and license_checker are removed and Python 3.14 is supported. There are slight changes in API and feature id generation. The current version will be replaced in one or two months. If you need no updates for the deployed machines in the future, you do not need to apply for a new license. We are also planning to add more features and support aarch64 in the future. Help us test the new version if you are interested! See the dev branch for the update.
• November 23, 2025 Support CUDA 12.8 and Python 3.11/3.12/3.13.
• August 1, 2024 Support Python 3.10.
• May 7, 2024 Add new features and flags to AnyGrasp detector:
  • Dense Predictions (default is False)
    * Set dense_grasp=True to enable extremely dense output. It's helpful for some corner cases or prompt-based grasping.
    * Warning: this mode is designed for special scenarios, leading to higher GPU memory, lower inference speed and lower grasp quality. You can crop the point clouds with your own segmantation masks or 3D bounding boxes to improve the performance.
  • Filtering by Objectness Mask (default is True)
    * Set apply_object_mask=False to disable default grasp filtering by objectness masks. This will lead to predictions on backgrounds.
  • Collision Detection (default is True)
    * Set collision_detection=False to disable default collision detection step.
  • These flags are useful for more flexible development, but we highly recommend to use the default setting in common scenarios. See grasp_detection/demo.py for examples.
• October 8, 2023 Fix a bug in grasp detection inference code, which may cause partial grasp widths exceeding the constrained range.
• 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 3.6-3.13
• PyTorch 1.7.1+ with CUDA 11.x/12.x
• MinkowskiEngine v0.5.4

Installation

1. Install Pytorch. Choose the appropriate version based on your environment.
2. Install MinkowskiEngine. We have modified MinkowskiEngine for better adpatation.

mkdir dependencies && cd dependencies conda install openblas-devel -c anaconda export CUDA_HOME=/path/to/cuda git clone git@github.com:chenxi-wang/MinkowskiEngine.git cd MinkowskiEngine

Uncomment the following line if you are using CUDA 12.x.

git checkout cuda-12-1

Uncomment the following line if you are using CUDA 12.8.

sed -i 's/\bauto __raw = __to_address(__r.get());/auto __raw = std::__to_address(__r.get());/' /usr/include/c++/11/bits/shared_ptr_base.h

python setup.py install --blas_include_dirs=${CONDA_PREFIX}/include --blas_library_dirs=${CONDA_PREFIX}/lib --blas=openblas cd ../..

1. Install other requirements from Pip.

   pip install -r requirements.txt

2. 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.

We usually reply in 5 workdays. If you do not receive the reply in 5 workdays, please check the spam folder.

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}
}