Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

11 Sep 2019  ·  Sulabh Kumra, Shirin Joshi, Ferat Sahin ·

In this paper, we present a modular robotic system to tackle the problem of generating and performing 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 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.

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Datasets

Cornell
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Robotic Grasping Cornell Grasp Dataset GR-ConvNet 5 fold cross validation 97.7 # 2
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Robotic Grasping Jacquard dataset GR-ConvNet Accuracy (%) 94.6 # 2
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