Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

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 an...

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Bibliographic Details
Main Authors: Kumra, Sulabh, Joshi, Shirin, Sahin, Ferat
Format: Conference Proceeding
Language:English
Subjects:
Convolutional neural networks
Grasping
Grippers
Manipulators
Real-time systems
Robots
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Summary: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.
ISSN:2153-0866
DOI:10.1109/IROS45743.2020.9340777