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ivalab / grasp_multiObject_multiGrasp

An implementation of our RA-L work 'Real-world Multi-object, Multi-grasp Detection'
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grasp grasping iros2018 multi-grasp-detection ra-l robotics

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grasp_multiObject_multiGrasp

This is the implementation of our RA-L work 'Real-world Multi-object, Multi-grasp Detection'. The detector takes RGB-D image input and predicts multiple grasp candidates for a single object or multiple objects, in a single shot. The original arxiv paper can be found here. The final version will be updated after publication process.

drawing

If you find it helpful for your research, please consider citing:

@inproceedings{chu2018deep,
  title = {Real-World Multiobject, Multigrasp Detection},
  author = {F. Chu and R. Xu and P. A. Vela},
  journal = {IEEE Robotics and Automation Letters},
  year = {2018},
  volume = {3},
  number = {4},
  pages = {3355-3362},
  DOI = {10.1109/LRA.2018.2852777},
  ISSN = {2377-3766},
  month = {Oct}
}

If you encounter any questions, please contact me at fujenchu[at]gatech[dot]edu

Demo

  1. Clone this repository

    git clone https://github.com/ivalab/grasp_multiObject_multiGrasp.git
cd grasp_multiObject_multiGrasp

  2. Build Cython modules

    cd lib
make clean
make
cd ..

  3. Install Python COCO API

    cd data
git clone https://github.com/pdollar/coco.git
cd coco/PythonAPI
make
cd ../../..

  4. Download pretrained models

    • trained model for grasp on dropbox drive
    • put under output/res50/train/default/

  5. Run demo

    ./tools/demo_graspRGD.py --net res50 --dataset grasp

    you can see images pop out.

Train

  1. Generate data
    1-1. Download Cornell Dataset
    1-2. Run dataPreprocessingTest_fasterrcnn_split.m (please modify paths according to your structure)
    1-3. Follow 'Format Your Dataset' section here to check if your data follows VOC format

  2. Train

    ./experiments/scripts/train_faster_rcnn.sh 0 graspRGB res50

ROS version?

Yes! please find it HERE

Acknowledgment

This repo borrows tons of code from

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