The hardness recognition is of great significance to tactile sensing and robotic control. The hardness recognition methods based on deep learning have demonstrated a good performance, however, a huge amount of manually labeled samples which require lots of time and labor costs are necessary for the training of deep neural networks. In order to alleviate this problem, a semi-supervised generative adversarial network (GAN) which requires less manually labeled samples is proposed in this paper. First of all, a large number of unlabeled samples are made use of through the unsupervised training of GAN, which is used to provide a good initial state to the following model. Afterwards, the manually labeled samples corresponding to each hardness level are individually used to train the GAN, of which the architecture and initial parameter values are inherited from the unsupervised GAN, and augmented by the generator of trained GAN. Finally, the hardness recognition network (HRN), of which the main architecture and initial parameter values are inherited from the discriminator of unsupervised GAN, is pretrained by a large number of augmented labeled samples and fine-tuned by manually labeled samples. The hardness recognition result can be obtained online by importing the tactile data captured by the robotic forearm into the trained HRN. The experimental results demonstrate that the proposed method can significantly save the manual labeling work while providing an excellent recognition precision for hardness recognition.

中文翻译：

---

基于半监督生成对抗网络的机器人前臂硬度识别。

硬度识别对触觉和机器人控制具有重要意义。基于深度学习的硬度识别方法已显示出良好的性能，但是，为进行深度神经网络的训练，需要大量时间和人工成本的大量手动标记样本。为了缓解这一问题，本文提出了一种半监督的生成对抗网络（GAN），该网络需要较少的人工标记样本。首先，通过GAN的无监督训练来利用大量未标记的样本，该样本用于为以下模型提供良好的初始状态。之后，分别使用与每个硬度级别相对应的手动标记的样本来训练GAN，其中的架构和初始参数值是从无监督的GAN继承的，并由训练有素的GAN生成器进行扩充。最终，硬度识别网络（HRN）的主要结构和初始参数值继承自无监督GAN的鉴别器，并通过大量增强标记的样本进行了预训练，并通过手动标记的样本进行了微调。硬度识别结果可以通过将机器人前臂捕获的触觉数据导入经过训练的HRN中来在线获得。实验结果表明，该方法可以大大节省人工标注的工作，同时为硬度识别提供了出色的识别精度。硬度识别网络（HRN）的主要结构和初始参数值继承自无监督GAN的判别器，并通过大量增强标记的样本进行了预训练，并通过手动标记的样本进行了微调。硬度识别结果可以通过将机器人前臂捕获的触觉数据导入经过训练的HRN中来在线获得。实验结果表明，该方法可以大大节省人工标注的工作，同时为硬度识别提供了出色的识别精度。硬度识别网络（HRN）的主要结构和初始参数值继承自无监督GAN的判别器，并通过大量增强标记的样本进行了预训练，并通过手动标记的样本进行了微调。硬度识别结果可以通过将机器人前臂捕获的触觉数据导入经过训练的HRN中来在线获得。实验结果表明，该方法可以大大节省人工标注的工作，同时为硬度识别提供了出色的识别精度。硬度识别结果可以通过将机器人前臂捕获的触觉数据导入经过训练的HRN中来在线获得。实验结果表明，该方法可以大大节省人工标注的工作，同时为硬度识别提供了出色的识别精度。硬度识别结果可以通过将机器人前臂捕获的触觉数据导入经过训练的HRN中来在线获得。实验结果表明，该方法可以大大节省人工标注的工作，同时为硬度识别提供了出色的识别精度。