Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
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In this paper, we tackle the problem of generating antipodal robotic grasps for unknown objects from 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 realtime speeds ( 20ms). We evaluate the proposed model architecture on standard datasets and previously unseen household objects. We achieved state-of-the-art accuracy of 97.7 demonstrate a 93.5 Our open-source implementation of GR-ConvNet can be found at github.com/skumra/robotic-grasping.
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