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Rotation invariance has been an important topic in computer vision tasks.Ideally, robot grasp detection should be rotation-invariant. However,rotation-invariance in robotic grasp detection has been only recently studiedby using rotation anchor box that are often time-consuming and unreliable formultiple objects. In this paper, we propose a rotation ensemble module (REM)for robotic grasp detection using convolutions that rotates network weights.Our proposed REM was able to outperform current state-of-the-art methods byachieving up to 99.2% (image-wise), 98.6% (object-wise) accuracies on theCornell dataset with real-time computation (50 frames per second). Our proposedmethod was also able to yield reliable grasps for multiple objects and up to93.8% success rate for the real-time robotic grasping task with a 4-axis robotarm for small novel objects that was significantly higher than the baselinemethods by 11-56%.

Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module