解决标注缺失与噪声下的抓取模型训练问题，提升鲁棒性。
Robust Loss Functions for Object Grasping under Limited Ground Truth
- 用预测类别概率配合伪标签处理无标注数据。
- 引入对称损失函数，有效抵抗标签噪声，提升精度2-13%。
- 方法简单易用，适合真实场景中数据不完整的抓取任务。
物体抓取是机器人感知并交互环境的关键技术。然而，在实际应用中，训练卷积神经网络时常面临标注缺失或噪声的问题，导致模型精度下降。为此，本文提出两种新损失函数以应对上述挑战：针对标注缺失，定义了一种针对未标注样本的预测类别概率方法，可与伪标签法协同高效工作；针对标签噪声，引入对称损失函数，能有效抵御标签污染。所提方法具有强鲁棒性和易用性。基于典型抓取神经网络的实验结果表明，该方法可使性能提升2%至13%。
原文摘要 · Abstract (English)
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.
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