此机械臂平面抓取算法是GRCNN,此版本是在原作者基础上加上自己的的一些配置和操作,只有plane_robotic_grasping这个文件夹的所有内容是额外添加的,详情请见plane_robotic_grasping/README.md
以下是原工程的README.md:
---
# Antipodal Robotic Grasping
We present a novel generative residual convolutional neural network based model architecture which detects objects in the camera’s field of view and predicts a suitable antipodal grasp configuration for the objects in the image.
This repository contains the implementation of the Generative Residual Convolutional Neural Network (GR-ConvNet) from the paper:
#### Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
Sulabh Kumra, Shirin Joshi, Ferat Sahin
[arxiv](https://arxiv.org/abs/1909.04810) | [video](https://youtu.be/cwlEhdoxY4U)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/antipodal-robotic-grasping-using-generative/robotic-grasping-on-cornell-grasp-dataset)](https://paperswithcode.com/sota/robotic-grasping-on-cornell-grasp-dataset?p=antipodal-robotic-grasping-using-generative)
If you use this project in your research or wish to refer to the baseline results published in the paper, please use the following BibTeX entry:
```
@inproceedings{kumra2020antipodal,
author={Kumra, Sulabh and Joshi, Shirin and Sahin, Ferat},
booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network},
year={2020},
pages={9626-9633},
doi={10.1109/IROS45743.2020.9340777}}
}
```
## Requirements
- numpy
- opencv-python
- matplotlib
- scikit-image
- imageio
- torch
- torchvision
- torchsummary
- tensorboardX
- pyrealsense2
- Pillow
## Installation
- Checkout the robotic grasping package
```bash
$ git clone https://github.com/skumra/robotic-grasping.git
```
- Create a virtual environment
```bash
$ python3.6 -m venv --system-site-packages venv
```
- Source the virtual environment
```bash
$ source venv/bin/activate
```
- Install the requirements
```bash
$ cd robotic-grasping
$ pip install -r requirements.txt
```
## Datasets
This repository supports both the [Cornell Grasping Dataset](https://www.kaggle.com/oneoneliu/cornell-grasp) and
[Jacquard Dataset](https://jacquard.liris.cnrs.fr/).
#### Cornell Grasping Dataset
1. Download the and extract [Cornell Grasping Dataset](https://www.kaggle.com/oneoneliu/cornell-grasp).
2. Convert the PCD files to depth images by running `python -m utils.dataset_processing.generate_cornell_depth <Path To Dataset>`
#### Jacquard Dataset
1. Download and extract the [Jacquard Dataset](https://jacquard.liris.cnrs.fr/).
## Model Training
A model can be trained using the `train_network.py` script. Run `train_network.py --help` to see a full list of options.
Example for Cornell dataset:
```bash
python train_network.py --dataset cornell --dataset-path <Path To Dataset> --description training_cornell
```
Example for Jacquard dataset:
```bash
python train_network.py --dataset jacquard --dataset-path <Path To Dataset> --description training_jacquard --use-dropout 0 --input-size 300
```
## Model Evaluation
The trained network can be evaluated using the `evaluate.py` script. Run `evaluate.py --help` for a full set of options.
Example for Cornell dataset:
```bash
python evaluate.py --network <Path to Trained Network> --dataset cornell --dataset-path <Path to Dataset> --iou-eval
```
Example for Jacquard dataset:
```bash
python evaluate.py --network <Path to Trained Network> --dataset jacquard --dataset-path <Path to Dataset> --iou-eval --use-dropout 0 --input-size 300
```
## Run Tasks
A task can be executed using the relevant run script. All task scripts are named as `run_<task name>.py`. For example, to run the grasp generator run:
```bash
python run_grasp_generator.py
```
## Run on a Robot
To run the grasp generator with a robot, please use our ROS implementation for Baxter robot. It is available at: https://github.com/skumra/baxter-pnp
没有合适的资源?快使用搜索试试~ 我知道了~
基于GRCNN的机械臂视觉平面抓取(python开发源码+项目说明).zip
共88个文件
py:34个
png:12个
txt:6个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
5星 · 超过95%的资源 1 下载量 51 浏览量
2024-03-10
21:30:00
上传
评论 3
收藏 72.49MB ZIP 举报
温馨提示
1、该资源内项目代码经过严格调试,下载即用确保可以运行! 2、该资源适合计算机相关专业(如计科、人工智能、大数据、数学、电子信息等)正在做课程设计、期末大作业和毕设项目的学生、或者相关技术学习者作为学习资料参考使用。 3、该资源包括全部源码,需要具备一定基础才能看懂并调试代码。 基于GRCNN的机械臂视觉平面抓取(python开发源码+项目说明).zip
资源推荐
资源详情
资源评论
收起资源包目录
基于GRCNN的机械臂视觉平面抓取(python开发源码+项目说明).zip (88个子文件)
project_code_0628
.DS_Store 8KB
inference
__init__.py 0B
.DS_Store 6KB
post_process.py 872B
grasp_generator.py 4KB
models
__init__.py 804B
grconvnet.py 2KB
grconvnet2.py 3KB
grasp_model.py 2KB
grconvnet3.py 3KB
grconvnet4.py 3KB
evaluate.py 7KB
hardware
__init__.py 0B
camera.py 2KB
device.py 582B
calibrate_camera.py 8KB
_config.yml 26B
run_calibration.py 377B
.gitattributes 240B
plane_robotic_grasping
.DS_Store 6KB
image
depth6.png 58KB
rgb5.png 309KB
depth1.png 60KB
rgb4.png 310KB
rgb3.png 303KB
rgb6.png 303KB
depth4.png 60KB
depth3.png 58KB
depth2.png 60KB
color_image.jpg 64KB
depth_image.jpg 61KB
depth5.png 61KB
rgb1.png 310KB
rgb2.png 310KB
cfg
camera_depth_scale.txt 18B
cam2base_H.csv 246B
camera_params.json 179B
robot.py 16KB
plane_robotic_grasping.py 7KB
README.md 465B
get_realsense_rgbd_image.py 3KB
utils
.DS_Store 6KB
timeit.py 969B
visualisation
gridshow.py 2KB
plot.py 5KB
data
__init__.py 366B
camera_data.py 3KB
cornell_data.py 3KB
jacquard_data.py 2KB
grasp_data.py 3KB
dataset_processing
evaluation.py 3KB
image.py 7KB
grasp.py 14KB
generate_cornell_depth.py 681B
get_jacquard.sh 206B
get_cornell.sh 305B
.idea
robotic-grasping.iml 489B
misc.xml 200B
inspectionProfiles
profiles_settings.xml 174B
modules.xml 284B
.gitignore 176B
docs
Antipodal Robotic Grasping using Generative Residu.pdf 3.43MB
Wang_Graspness_Discovery_in_Clutters_for_Fast_and_Accurate_Grasp_Detection_ICCV_2021_paper.pdf 1.75MB
run_grasp_generator.py 279B
run_offline.py 3KB
requirements.txt 224B
.gitignore 1KB
cleanup.sh 173B
trained-models
.DS_Store 8KB
cornell-randsplit-rgbd-grconvnet3-drop1-ch32
epoch_13_iou_0.96 7.29MB
epoch_15_iou_0.97 7.29MB
epoch_19_iou_0.98 7.29MB
arch.txt 3KB
jacquard-rgbd-grconvnet3-drop0-ch32
epoch_42_iou_0.93 7.31MB
epoch_35_iou_0.92 7.31MB
epoch_48_iou_0.93 7.31MB
arch.txt 3KB
cornell-randsplit-rgbd-grconvnet3-drop1-ch16
epoch_20_iou_0.97 1.86MB
epoch_30_iou_0.97 1.86MB
epoch_17_iou_0.96 1.86MB
arch.txt 3KB
jacquard-d-grconvnet3-drop0-ch32
epoch_50_iou_0.94 7.28MB
epoch_48_iou_0.93 7.28MB
arch.txt 3KB
epoch_44_iou_0.93 7.28MB
README.md 4KB
run_realtime.py 3KB
train_network.py 13KB
共 88 条
- 1
资源评论
- 努力成为CEer的菜鸟2024-03-19这个资源总结的也太全面了吧,内容详实,对我帮助很大。
辣椒种子
- 粉丝: 3464
- 资源: 5723
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功