论文标题
基于姿势的触觉伺服：使用深度学习控制的柔软触摸
Pose-Based Tactile Servoing: Controlled Soft Touch using Deep Learning
论文作者
论文摘要
本文介绍了一种使用软触觉传感器来控制机器人的新方法：基于姿势的触觉伺服（PBTS）控制。基本思想是嵌入触觉感知模型，以估算传感器姿势在伺服控制环内，该伺服控制循环应用于局部对象特征，例如边缘和表面。 PBTS控制使用软曲面光学触觉传感器（BRL Tactip）实施，该卷积神经网络训练有素，对剪切不敏感。因此，实现了各种复杂3D对象的稳健和准确的受控运动。首先，我们在对PBTS的正式控制之前，回顾一下触觉伺服及其与视觉致毒的关系。然后，我们评估在一系列规则和不规则物体上的触觉伺服。最后，我们反思与视觉伺服器控制的关系，并讨论受控的软接触如何为机器人中的人类敏捷性提供途径。
This article describes a new way of controlling robots using soft tactile sensors: pose-based tactile servo (PBTS) control. The basic idea is to embed a tactile perception model for estimating the sensor pose within a servo control loop that is applied to local object features such as edges and surfaces. PBTS control is implemented with a soft curved optical tactile sensor (the BRL TacTip) using a convolutional neural network trained to be insensitive to shear. In consequence, robust and accurate controlled motion over various complex 3D objects is attained. First, we review tactile servoing and its relation to visual servoing, before formalising PBTS control. Then, we assess tactile servoing over a range of regular and irregular objects. Finally, we reflect on the relation to visual servo control and discuss how controlled soft touch gives a route towards human-like dexterity in robots.
