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We propose to take a novel approach to robot system design where eachbuilding block of a larger system is represented as a differentiable program,i.e. a deep neural network. This representation allows for integratingalgorithmic planning and deep learning in a principled manner, and thus combinethe benefits of model-free and model-based methods. We apply the proposedapproach to a challenging partially observable robot navigation task. The robotmust navigate to a goal in a previously unseen 3-D environment without knowingits initial location, and instead relying on a 2-D floor map and visualobservations from an onboard camera. We introduce the Navigation Networks(NavNets) that encode state estimation, planning and acting in a single,end-to-end trainable recurrent neural network. In preliminary simulationexperiments we successfully trained navigation networks to solve thechallenging partially observable navigation task.

Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation