Acknowledgement

The main part of the code is cloned from https://github.com/CZ-Wu/GPNet and I applied it to my project.

Get grasps

You need to set the input point cloud data path, weight path and output txt/npy data path in demo.py. Run the following command:

python demo.py

Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps

Pytorch implementation of GPNet.

Environment

Ubuntu 16.04
pytorch 0.4.1
CUDA 8.0 or CUDA 9.2

Our depth images are saved in .exr files, please install the OpenEXR, then run pip install OpenEXR.

Build CUDA kernels

cd lib/pointnet2
mkdir build && cd build
cmake .. && make

Dataset

Our dataset is available at Google Driver. Backup (2qln).

Simulation Environment

The simulation environment is built on PyBullet. You can use pip to install the python packages:

pip install pybullet
pip install attrdict
pip install collections
pip install joblib
pip install gc

Please look for the details of our simulation configurations in the directory simulator.

Training

CUDA_VISIBLE_DEVICES=0,1 python train --tanh --grid --dataset_root path_to_dataset

Test

CUDA_VISIBLE_DEVICES=0,1 python test --tanh --grid --dataset_root path_to_dataset --resume pretrained_model/checkpoint_440.pth.tar

Then it will generate the predicted grasps saved in .npz files in pretrained_model/test/epoch440/view0. The file pretrained_model/test/epoch440/nms_poses_view0.txt contains the predicted grasps after nms.

Rule-based evaluation

You can use the following script to abtain the success rate and coverage rate.

CUDA_VISIBLE_DEVICES=0 python topk_percent_coverage_precision.py -pd pretrained_model/test/epoch440/view0 -gd path_to_gt_annotations

Simulation-based evaluation

To test the predicted grasps in simulation environment:

cd simulator
python -m simulateTest.simulatorTestDemo -t pretrained_model/test/epoch440/nms_poses_view0.txt

Citation

@article{wu2020grasp,
  title={Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps},
  author={Wu, Chaozheng and Chen, Jian and Cao, Qiaoyu and Zhang, Jianchi and Tai, Yunxin and Sun, Lin and Jia, Kui},
  journal={arXiv preprint arXiv:2009.12606},
  year={2020}
}