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.

翻译：文章描述了一种使用软触动传感器控制机器人的新方式: 以布局为基础的触动瑟沃( PBTS) 控制。 基本的想法是将一个触动感知模型嵌入一个用于估计传感器在用于边缘和表面等局部物体特征的静脉控制环内。 PBTS 控制使用一个软曲线光学触动传感器( BRL TacTip ), 使用经过训练对剪切不敏感的电动神经网络进行控制。 因此, 对各种复杂的 3D 对象实现了稳健和准确的控控动。 首先, 我们在将 PBTS 控制正规化之前, 检查电动静动及其与视觉振动的关系。 然后, 我们评估一连串常规物体和不正常物体的电动静脉动。 最后, 我们思考与视觉静脉控制的关系, 并讨论控制软触如何给机器人带来像人类一样的易感。