  We propose to take a novel approach to robot system design where each
building block of a larger system is represented as a differentiable program,
i.e. a deep neural network. This representation allows for integrating
algorithmic planning and deep learning in a principled manner, and thus combine
the benefits of model-free and model-based methods. We apply the proposed
approach to a challenging partially observable robot navigation task. The robot
must navigate to a goal in a previously unseen 3-D environment without knowing
its initial location, and instead relying on a 2-D floor map and visual
observations 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 simulation
experiments we successfully trained navigation networks to solve the
challenging partially observable navigation task.
