Humans detect tactile stimuli through a combination of pressure and vibration signals using different types of cutaneous receptor. The development of artificial tactile perception systems is of interest in the development of robotics and prosthetics, and artificial receptors, nerves and skin have been created. However, constructing systems with human-like capabilities remains challenging. Here, we report an artificial neural tactile skin system that mimics the human tactile recognition process using particle-based polymer composite sensors and a signal-converting system. The sensors respond to pressure and vibration selectively, similarly to slow adaptive and fast adaptive mechanoreceptors in human skin, and can generate sensory neuron-like output signal patterns. We show in an ex vivo test that undistorted transmission of the output signals through an afferent tactile mouse nerve fibre is possible, and in an in vivo test that the signals can stimulate a rat motor nerve to induce the contraction of a hindlimb muscle. We use our tactile sensing system to develop an artificial finger that can learn to classify fine and complex textures by integrating the sensor signals with a deep learning technique. The approach can also be used to predict unknown textures on the basis of the trained model.

人类使用不同类型的皮肤受体通过压力和振动信号的组合来检测触觉刺激。人工触觉感知系统的发展对机器人和假肢的发展很感兴趣，人工感受器、神经和皮肤已经被创造出来。然而，构建具有类人能力的系统仍然具有挑战性。在这里，我们报告了一种人工神经触觉皮肤系统，该系统使用基于粒子的聚合物复合传感器和信号转换系统来模拟人类触觉识别过程。传感器选择性地响应压力和振动，类似于人体皮肤中的慢自适应和快速自适应机械感受器，并且可以产生类似感觉神经元的输出信号模式。我们在体外测试中表明，输出信号通过传入的触觉小鼠神经纤维进行无失真传输是可能的，并且在体内测试中，信号可以刺激大鼠运动神经以诱导后肢肌肉的收缩。我们使用我们的触觉传感系统开发了一种人造手指，它可以通过将传感器信号与深度学习技术相结合来学习对精细和复杂的纹理进行分类。该方法还可用于基于训练模型预测未知纹理。我们使用我们的触觉传感系统开发了一种人造手指，它可以通过将传感器信号与深度学习技术相结合来学习对精细和复杂的纹理进行分类。该方法还可用于基于训练模型预测未知纹理。我们使用我们的触觉传感系统开发了一种人造手指，它可以通过将传感器信号与深度学习技术相结合来学习对精细和复杂的纹理进行分类。该方法还可用于基于训练模型预测未知纹理。