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 | video

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:

@article{kumra2019antipodal,
  title={Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network},
  author={Kumra, Sulabh and Joshi, Shirin and Sahin, Ferat},
  journal={arXiv preprint arXiv:1909.04810},
  year={2019}
}

Requirements

• numpy
• opencv-python
• matplotlib
• scikit-image
• imageio
• torch
• torchvision
• torchsummary
• tensorboardX
• pyrealsense2
• Pillow

Installation

• Checkout the robotic grasping package

$ git clone https://github.com/skumra/robotic-grasping.git

• Create a virtual environment

$ python3.6 -m venv --system-site-packages venv

• Source the virtual environment

$ source venv/bin/activate

• Install the requirements

$ cd robotic-grasping
$ pip install -r requirements.txt

Run Tasks

Run the relevant task using the run programs. For example, to run the grasp generator run:

python run_grasp_generator.py

Run on a Robot

Our ROS implementation for running the grasp generator with Baxter robot is available at: https://github.com/skumra/baxter-pnp