This article illustrates the application of deep learning to robot touch by considering a basic yet fundamental capability: estimating the relative pose of part of an object in contact with a tactile sensor. We begin by surveying deep learning applied to tactile robotics, focusing on optical tactile sensors, which help to link touch and deep learning for vision. We then show how deep learning can be used to train accurate pose models of 3D surfaces and edges that are insensitive to nuisance variables, such as motion-dependent shear. This involves including representative motions as unlabeled perturbations of the training data and using Bayesian optimization of the network and training hyperparameters to find the most accurate models. Accurate estimation of the pose from touch will enable robots to safely and precisely control their physical interactions, facilitating a wide range of object exploration and manipulation tasks.

中文翻译：

---

机器人触摸的最佳深度学习：训练3D表面和边缘的精确姿势模型

本文通过考虑一种基本但基本的功能来说明深度学习在机器人触摸中的应用：估算与触觉传感器接触的对象部分的相对姿势。我们首先研究应用于触觉机器人的深度学习，重点是光学触觉传感器，这有助于将触摸和深度学习联系起来。然后，我们展示了深度学习如何用于训练对扰动变量（例如与运动有关的剪切）不敏感的3D表面和边缘的精确姿势模型。这涉及将代表性运动包括为训练数据的未标记扰动，并使用网络的贝叶斯优化和训练超参数来找到最准确的模型。