manohiro / grasp_multiObject_multiGrasp

An implementation of our RA-L work 'Real-world Multi-object, Multi-grasp Detection'

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.

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

Acknowledgment

This repo borrows tons of code from

• tf-faster-rcnn by endernewton

Resources

• multi-object grasp dataset
• grasp annotation tool