基于python实现的GRCNN的机械臂视觉平面抓取

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 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 ```