This paper addresses the recognition of daily-life objects by a robot equipped with tactile sensors. The main contribution is a deep learning framework that can recognize objects already touched as well as objects never touched before. To this end, we train a deconvolutional neural network that generates synthetic tactile data for novel classes. Then, we use both these synthetic data and the real data collected by touching objects, to train a convolutional neural network to recognize both known (trained) objects and novel objects. Furthermore, we propose a method for integrating newly encountered data into novel classes. Finally, we evaluate the framework using the largest available dataset of tactile objects descriptions.

中文翻译：

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用于已知和新颖对象的触觉识别的深度学习框架

本文致力于通过配备触觉传感器的机器人识别日常生活中的物体。主要的贡献是深度学习框架，它可以识别已经接触过的对象以及从未接触过的对象。为此，我们训练了一个反卷积神经网络，该网络为新颖的类生成合成的触觉数据。然后，我们使用这些合成数据和通过触摸对象收集的真实数据，来训练卷积神经网络，以识别已知（训练过的）对象和新颖的对象。此外，我们提出了一种将新遇到的数据集成到新颖类中的方法。最后，我们使用最大的触觉对象描述数据集评估框架。