This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision sensor "frames" that consist of a constant number of DAVIS ON and OFF events. The network is thus "data driven" at a sample rate proportional to the scene activity, so the effective sample rate varies from 15 Hz to 240 Hz depending on the robot speeds. The network generates four outputs: steer right, left, center and non-visible. After off-line training on labeled data, the network is imported on the on-board Summit XL robot which runs jAER and receives steering directions in real time. Successful results on closed-loop trials, with accuracies up to 87% or 92% (depending on evaluation criteria) are reported. Although the proposed approach discards the precise DAVIS event timing, it offers the significant advantage of compatibility with conventional deep learning technology without giving up the advantage of data-driven computing.

描述了在掠食者/猎物情境下，应用卷积神经网络(CNN)的情形。该网络以动态和主动像素传感器(DAVIS)上的数据为基础，由常规图像帧和动态视觉传感器帧驱动，并根据场景活动的采样率生成四个输出。实验结果表明，该网络在机器人实时接收到方向指示时能够得出高达87％或92％的精度。

使用混合框架/事件驱动卷积神经网络来操纵捕食机器人