使用 GR-ConvNet 的反足机器人抓取_python_代码_下载

# 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) [](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