Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

Sulabh Kumra; Shirin Joshi; Ferat Sahin

IROS 2020poster402 citations

Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

Abstract

In this paper, we present a modular robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from the n-channel image of the scene. We propose a novel Generative Residual Convolutional Neural Network (GR-ConvNet) model that can generate robust antipodal grasps from n-channel input at real-time speeds (~20ms). We evaluate the proposed model architecture on standard datasets and a diverse set of household objects. We achieved state-of-the-art accuracy of 97.7% and 94.6% on Cornell and Jacquard grasping datasets, respectively. We also demonstrate a grasp success rate of 95.4% and 93% on household and adversarial objects, respectively, using a 7 DoF robotic arm.

BibTeX

@inproceedings{iros2020_antipodalrobotic,
  title = {Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network},
  author = {Sulabh Kumra and Shirin Joshi and Ferat Sahin},
  booktitle = {IROS 2020},
  year = {2020}
}