Traditional approaches to sensing have often been aimed at simple sensor characteristics to make interpretation of the sensor outputs easier, but this has also limited the quality of the encoded sensory information. Integrating a complex sensor with deep learning could hence be a strategy for removing current limitations on the information that sensory inputs can carry. Here, we demonstrate this concept with a soft-robotic sensor that mimics fast non-rigid deformation of the ears in certain bat species. We show that a deep convolutional neural network can use the nonlinear Doppler shift signatures generated by these motions to estimate the direction of a sound source with an estimation error of ~0.5°. Previously, determining the direction of a sound source based on pressure receivers required either multiple frequencies or multiple receivers. Our current results demonstrate a third approach that makes do with only a single frequency and a single receiver.

传统的传感方法通常针对简单的传感器特性，以使传感器输出的解释更容易，但这也限制了编码的感觉信息的质量。因此，将复杂传感器与深度学习相结合可能是一种消除当前对感官输入可以携带的信息的限制的策略。在这里，我们用一个软机器人传感器演示了这个概念，该传感器模拟了某些蝙蝠物种耳朵的快速非刚性变形。我们表明，深度卷积神经网络可以使用这些运动产生的非线性多普勒频移特征来估计声源的方向，估计误差约为 0.5°。以前，根据压力接收器确定声源的方向需要多个频率或多个接收器。