论文标题
基于GRASP回归的得分抓地力，以更好地掌握预测
Scoring Graspability based on Grasp Regression for Better Grasp Prediction
论文作者
论文摘要
抓住对象是机器人为了与环境互动而需要掌握的最重要能力之一。当前的最新方法依赖于经过训练的深神经网络，以共同预测抓地力得分，以及相对于GRASP参考参数的偏移的回归。但是，这两个预测是独立执行的，在应用预测偏移时，实际的抓地力得分可能会降低。因此，在本文中，我们扩展了一个最先进的神经网络，该神经网络具有评估给定位置的抓地力，并引入了一种新型损失函数，该损失函数将GRASP参数的回归与掌握性得分相关联。我们表明，这种新颖的体系结构将最先进的GRASP检测网络的性能从82.13％提高到Jacquard数据集的85.74％。当学习的模型被转移到真实机器人上时，提出的方法将抓地力和掌握回归的速度相关联，而在没有相关性的基线训练的基线中为88.1％。
Grasping objects is one of the most important abilities that a robot needs to master in order to interact with its environment. Current state-of-the-art methods rely on deep neural networks trained to jointly predict a graspability score together with a regression of an offset with respect to grasp reference parameters. However, these two predictions are performed independently, which can lead to a decrease in the actual graspability score when applying the predicted offset. Therefore, in this paper, we extend a state-of-the-art neural network with a scorer that evaluates the graspability of a given position, and introduce a novel loss function which correlates regression of grasp parameters with graspability score. We show that this novel architecture improves performance from 82.13% for a state-of-the-art grasp detection network to 85.74% on Jacquard dataset. When the learned model is transferred onto a real robot, the proposed method correlating graspability and grasp regression achieves a 92.4% rate compared to 88.1% for the baseline trained without the correlation.
