/anygrasp_sdk

Primary LanguagePython

AnyGrasp SDK

AnyGrasp SDK for grasp detection & tracking.

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

Video

IMAGE ALT TEXT
AnyGrasp cleaning fragments of a broken pot

IMAGE ALT TEXT
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
  1. 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.

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{fang2022anygrasp,
  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={arXiv preprint arXiv:2212.08333},
  year={2022}
}

@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}
}