REGNet_for_3D_Grasping

RGENet (ICRA 2021, arXiv) is a REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds. It aims at generating the optimal grasp of novel objects from partial noisy observations.

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Install (Using a virtual environment is recommended)

1. Clone the repository

cd ${your_project_path}
git clone https://github.com/zhaobinglei/REGNet_for_3D_Grasping.git

2. Install the requirements in requirements.txt
3. Install the utils of pointnet++

cd REGNet_for_3D_Grasping/multi_model/utils/pn2_utils
python setup.py install
cd functions
python setup.py install

4. Modify the path of models and datasets in train.py and test.py

'--load-score-path'  : The path of pretrained ScoreNet model 
'--load-region-path': The path of pretrained GraspRegionNet and RefineNet model 
'--data-path'       : The dataset path
'--model-path'      : to saved model path
'--log-path'        : to saved log path
'--folder-name'     : the folder path of the point cloud file to be tested
'--file-name'       : the name the point cloud file to be tested. If use the default path, it will test all files in the folder-name.

5. Modify the parameters of gripper and enviroment configuration in train.py and test.py

width, height, depth: the parameters of the two-finger grippers
table_height        : the height of the table

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Data preparation

The link of the new dataset is https://pan.baidu.com/s/1alwaKQZt0IGE11FFSClxOg . And the extraction code is x79a. (Maybe you can only use a Chinese proxy to access the link.) The dataset used for paper writing has less grasp annotations. The grasps in the new dataset can better cover the objects in the environment. Note that the scenes and rendered point clouds in the old and new dataset are the same.

The new model in /assets/models/final is trained using the new dataset.

(The dataset is too huge. So please be patient when downloading.)

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Training

The architecture of REGNet is shown in follows.

1. Train (Recommand training the three stages based on the pretrained models. If using the pretrained models, please modify the path of models and datasets in train.py.)

cd REGNet_for_3D_Grasping/
python train.py --gpu 0 --gpu-num 3 --gpus 0,1,2 --batch-size 15 --mode train --tag regnet

2. Pretrain (You can train the SN and GRN separately or at the same time.)

cd REGNet_for_3D_Grasping/
python train.py --gpu 0 --gpu-num 3 --gpus 0,1,2 --batch-size 15 --mode pretrain_score --tag regnet_score
python train.py --gpu 0 --gpu-num 3 --gpus 0,1,2 --batch-size 15 --mode pretrain_region --tag regnet_region

3. Launch tensorboard for monitoring

cd REGNet_for_3D_Grasping/assets/log
tensorboard --logdir . --port 8080

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Test and Visualization

1. Test the file in test_file/real_data or test_file/virtual_data. The corresponding predict results are saved in test_file/real_data_predict or test_file/virtual_data_predict

cd REGNet_for_3D_Grasping/
python test.py --gpu 7 --gpu-num 1

2. visulation

cd REGNet_for_3D_Grasping/vis
python vis_grasp.py 

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Results

The validation logging file is shown in below figures.

After refinement, the performance has improved significantly.

There are some results predicted by REGNet. The first situation is in the validation dataset, which has the same distribution as the training dataset (table_height=0.75). Some objects do not have the corresponding grasps, since the gripper's width is smaller than the object's size. The red grasp has the highest predicted score.

The second situation is also generated in simulation enviroment. But it uses the objects' models that don't in the training dataset (table_height=0.5).

The next two pictures are observed by Kinect in the real world (table_height=0.7). The objects also don't in the training dataset.

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Future Work

Our future work will optimize the code to make it easier to train. Please continue to pay attention. ~