Object grasping is a crucial technology enabling robots to perceive and interact with the environment sufficiently. However, in practical applications, researchers are faced with missing or noisy ground truth while training the convolutional neural network, which decreases the accuracy of the model. Therefore, different loss functions are proposed to deal with these problems to improve the accuracy of the neural network. For missing ground truth, a new predicted category probability method is defined for unlabeled samples, which works effectively in conjunction with the pseudo-labeling method. Furthermore, for noisy ground truth, a symmetric loss function is introduced to resist the corruption of label noises. The proposed loss functions are powerful, robust, and easy to use. Experimental results based on the typical grasping neural network show that our method can improve performance by 2 to 13 percent.

翻译：物体抓取是使机器人能够充分感知环境并与之交互的关键技术。然而在实际应用中，研究者在训练卷积神经网络时面临着真值数据缺失或含噪的问题，这会降低模型的准确性。为此，本文提出了不同的损失函数来处理这些问题，以提升神经网络的精度。针对真值缺失的情况，我们为未标注样本定义了一种新的预测类别概率方法，该方法与伪标注方法结合使用效果显著。此外，针对含噪真值，我们引入了一种对称损失函数以抵抗标签噪声的干扰。所提出的损失函数功能强大、鲁棒性好且易于使用。基于典型抓取神经网络的实验结果表明，我们的方法能将性能提升2%至13%。