To reduce the spatial dimensional inaccuracy due to upsampling in the traditional CNN framework, we develop a novel grasping visual architecture referred to as High resolution grasp nerual network (HRG-Net), a parallel-branch structure that always maintains a high-resolution representation and repeatedly exchanges information across resolutions.
This repository contains the implementation of the High Resolution Grasp Nerual Network(HRG-Net) from the paper: A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions
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If you use this work, please cite:
@article{zhou2022robotic,
title={A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions},
author={Zhou, Zhangli and Wang, Shaochen and Chen, Ziyang and Cai, Mingyu and Kan, Zhen},
journal={arXiv preprint arXiv:2209.07459},
year={2022}
}
conda create -n hrgnet python==3.8
conda activate hrgnet
pip install -r requirements.txt / conda env create -f environment.yaml
Cornell | Jacquard | multiobject(Just for test)
python train_hrgnet.py
For validation and visualization purposes, we provide our previously trained model
python evaluation_grasp.py # For Cornell and Jacquard dataset
python evaluation_heatmap.py # For Cornell and Jacquard dataset
python multi_grasp_visualization.py # For multiobject dataset
Code heavily inspired and modified from https://github.com/dougsm/ggcnn. The code for the experiments related to the robot in the physical environment will be released later. For more of our recent work please follow link