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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 datafrom 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 bothconventional image frames and dynamic vision sensor "frames" that consist of aconstant 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 samplerate varies from 15 Hz to 240 Hz depending on the robot speeds. The networkgenerates four outputs: steer right, left, center and non-visible. Afteroff-line training on labeled data, the network is imported on the on-boardSummit 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 approachdiscards the precise DAVIS event timing, it offers the significant advantage ofcompatibility with conventional deep learning technology without giving up theadvantage of data-driven computing.

Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network