Awesome
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.
<p align="center"> <img src="https://github.com/ivalab/grasp_multiObject_multiGrasp/blob/master/fig/multi_grasp_multi_object.png" alt="drawing" width="300"/> </p>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
- Clone this repository
git clone https://github.com/ivalab/grasp_multiObject_multiGrasp.git
cd grasp_multiObject_multiGrasp
- Build Cython modules
cd lib
make clean
make
cd ..
- Install Python COCO API
cd data
git clone https://github.com/pdollar/coco.git
cd coco/PythonAPI
make
cd ../../..
- Download pretrained models
- trained model for grasp on dropbox drive
- put under
output/res50/train/default/
- Run demo
./tools/demo_graspRGD.py --net res50 --dataset grasp
you can see images pop out.
Train
-
Generate data
1-1. Download Cornell Dataset
1-2. RundataPreprocessingTest_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 -
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
- tf-faster-rcnn by endernewton