AFFGA-Net

The AFFGA-Net is a high performance network which predicts the quality and pose of grasps at every pixel in an input RGB image.

This repository contains the data set used to train AFFGA-Net and the program for labeling the grasp model.

High-performance Pixel-level Grasp Detection based on Adaptive Grasping and Grasp-aware Network

Dexin Wang, Chunsheng Liu, Faliang Chang, Nanjun Li, and Guangxin Li

This paper has been accepted by IEEE Trans. Ind. Electron.

TechRxiv | Video

Installation

This code was developed with Python 3.6 on Ubuntu 16.04. The main Python requirements:

pytorch==1.2 or higher version
opencv-python
mmcv
numpy

Datasets

1. Download and extract Cornell and Clutter Dataset.

2. run generate_grasp_mat.py，convert pcd*Label.txt to pcd*grasp.mat, they represent the same label, but the format is different, which is convenient for AFFGA-Net to read.

3. Put all the samples of the Cornell and Clutter datasets in the same folder, and put train-test folder in the upper directory of the dataset, as follows

   D:\path_to_dataset\
   ├─cornell_clutter
   │  ├─pcd0100grasp.mat
   │  └─pcd0100r.png
   │  |
   |  └─pcd2000grasp.mat
   |  └─pcd2000r.png
   |
   ├─train-test
   │  ├─train-test-all
   │  ├─train-test-cornell
   │  └─train-test-mutil
   │  └─train-test-single
   |
   ├─other_files
   

Pre-trained Model

Some example pre-trained models for AFFGA-Net can be downloaded from here.

The model is trained on the Cornell and Clutter dataset using the RGB images.

The zip file contains the full saved model from torch.save(model, path).

Training

Training is done by the train_net.py script.

Some basic examples:

python train_net.py --dataset-path <Path To Dataset>

Trained models are saved in output/models by default, with the validation score appended.

Visualisation

visualisation of the trained networks are done using the demo.py script.

Modify the path of the pre-trained model before running, i.e. model.

Some output examples of AFFGA-Net is under the demo\output.

Running on a kinova Robot

future work