Journal
2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Volume -, Issue -, Pages 9626-9633Publisher
IEEE
DOI: 10.1109/IROS45743.2020.9340777
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- Rochester Institute of Technology
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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 (similar to 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.
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