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Revisiting End-to-end Deep Learning for Obstacle Avoidance: Replication and Open Issues

Obstacle avoidance is an essential feature for autonomous robots. It is usually addressed with specialized sensors and Simultaneous Localization and Mapping algorithms (SLAM, Cadena et al. (2016)). Muller et al. (2006) have demonstrated that it can also be addressed using end-to-end deep learning. They proposed a convolutional neural network that maps raw stereo pair input images to steering outputs and is trained by a human driver in an outdoor setting. Using the ToyCollect open source hardware and software platform, we replicate their main findings, compare several variants of their network that differ in the way steering angles are represented, and extend their system to indoor obstacle avoidance. We discuss several issues for further work concerning the automated generation of training data and the quantitative evaluation of such systems.