基于抓取回归的抓取能力评分方法
Scoring Graspability based on Grasp Regression for Better Grasp Prediction
摘要：
抓取物体是机器人与环境交互所需要掌握的最重要的能力之一。当前最先进的方法依赖于深度神经网络，所述深度神经网络被训练以联合预测可抓握性分数以及相对于抓握参考参数的偏移的回归。然而，这两个预测是独立执行的，这可能导致在应用预测的偏移时实际可抓握性分数的降低。因此，在本文中，我们扩展了一个国家的最先进的神经网络的评分器，评估一个给定的位置的可抓握性，并引入了一种新的损失函数，相关的抓地力得分参数的回归。我们发现，这种新的架构将性能从最先进的抓取检测网络的82.13%提高到Jobserver数据集的85.74%。当学习到的模型被转移到一个真正的机器人，所提出的方法相关的可抓取性和把握回归达到了92.4%的率相比，88.1%的基线训练没有相关性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
