论文标题
机器人触摸的最佳深度学习
Optimal Deep Learning for Robot Touch
论文作者
论文摘要
本文说明了通过考虑基本但基本的功能：估计与触觉传感器接触的对象的相对姿势的应用到机器人触摸的应用。首先，我们调查应用于触觉机器人技术的深度学习，重点关注光学触觉传感器，这有助于从深度学习中桥梁以触摸触摸。然后，我们展示如何使用深度学习来训练对3D表面和边缘的精确姿势模型，这些模型对诸如运动依赖性剪切等滋扰变量不敏感。这涉及包括代表性动议作为训练数据的未标记扰动，并使用网络和训练超参数的贝叶斯优化来找到最准确的模型。从触摸中对姿势的准确估计将使机器人能够安全，精确地控制其物理相互作用，从而实现广泛的对象探索和操纵任务。
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, focussing on optical tactile sensors, which help bridge from deep learning for vision to touch. 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 unlabelled perturbations of the training data and using Bayesian optimization of the network and training hyperparameters to find the most accurate models. Accurate estimation of pose from touch will enable robots to safely and precisely control their physical interactions, underlying a wide range of object exploration and manipulation tasks.
