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

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 studied by using rotation anchor box that are often time-consuming and unreliable for multiple 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 by achieving up to 99.2% (image-wise), 98.6% (object-wise) accuracies on the Cornell dataset with real-time computation (50 frames per second). Our proposed method was also able to yield reliable grasps for multiple objects and up to 93.8% success rate for the real-time robotic grasping task with a 4-axis robot arm for small novel objects that was significantly higher than the baseline methods by 11-56%.

I. INTRODUCTION

Robot grasping of novel objects has been investigated extensively, but it is still a challenging open problem in robotics. Humans instantly identify multiple grasps of novel objects (perception), plan how to pick them up (planning) and actually grasp it reliably (control). However, accurate robotic grasp detection, trajectory planning and reliable execution are quite challenging for robots. As the first step, detecting robotic grasps accurately and quickly from imaging sensors is an important task for successful robotic grasping.