Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

By Sulabh Kumra, Shirin Joshi, Ferat Sahin. IEEE/RJS International Conference on Intelligent RObots and Systems, 2019

399 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/27b4c1f3e5edb864a8d99ea3e269ed28578e5d33

How researchers see this paper

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In Biomedical Engineering

1. Shiguo Lian
2. Kaiyong Zhao
3. Kai Wang
4. Guoguang Du

In Control and Systems Engineering

1. Shiguo Lian
2. Kaiyong Zhao
3. Kai Wang
4. Guoguang Du

Citations in the corpus

• Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review (2020, background): “[Kumra et al., 2019] proposed a novel Generative Residual Convolutional Neural Network (GR-ConvNet) model that can generate robust antipodal grasps from a n-channel image input.”