论文标题
VGQ-CNN：超越固定摄像机和顶级抓取质量预测
VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction
论文作者
论文摘要
我们介绍了多功能的掌握质量卷积神经网络（VGQ-CNN），这是一个用于6-DOF GRASP的掌握质量预测网络。 VGQ-CNN在评估grasps的对象时可以使用VGQ-CNN，而无需重新训练网络。通过明确定义GRASP方向作为网络的输入，VGQ-CNN可以评估6-DOF抓取姿势，超越了大多数基于图像的GRASP评估方法（如GQ-CNN）中使用的4-DOF GRASP。为了训练VGQ-CNN，我们生成了新的Versatile Grasp数据集（VG-DSET），其中包含从各种相机姿势中观察到的6-DOF GRASP。 VGQ-CNN在我们的测试分段中达到了82.1％的平衡精度，同时将其推广到各种相机姿势。同时，与GQ-CNN的76.6％相比，它以74.2％的均衡精度达到了高架摄像机和顶级摄像机的竞争性能。我们还提出了一个修改的网络体系结构快速VGQ-CNN，该网络架构使用共享的编码器体系结构加快推理，并可以在CPU上进行128个掌握质量预测。代码和数据可在https://aucoroboticsmu.github.io/vgq-cnn/上找到。
We present the Versatile Grasp Quality Convolutional Neural Network (VGQ-CNN), a grasp quality prediction network for 6-DOF grasps. VGQ-CNN can be used when evaluating grasps for objects seen from a wide range of camera poses or mobile robots without the need to retrain the network. By defining the grasp orientation explicitly as an input to the network, VGQ-CNN can evaluate 6-DOF grasp poses, moving beyond the 4-DOF grasps used in most image-based grasp evaluation methods like GQ-CNN. To train VGQ-CNN, we generate the new Versatile Grasp dataset (VG-dset) containing 6-DOF grasps observed from a wide range of camera poses. VGQ-CNN achieves a balanced accuracy of 82.1% on our test-split while generalising to a variety of camera poses. Meanwhile, it achieves competitive performance for overhead cameras and top-grasps with a balanced accuracy of 74.2% compared to GQ-CNN's 76.6%. We also propose a modified network architecture, FAST-VGQ-CNN, that speeds up inference using a shared encoder architecture and can make 128 grasp quality predictions in 12ms on a CPU. Code and data are available at https://aucoroboticsmu.github.io/vgq-cnn/.
