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.

提出了一种机器人系统的新方法，其中一个较大系统的每个构件都表示为可微分的程序，即深度神经网络，应用到一个具有挑战性的部分可观测机器人导航任务中。在预测性模拟实验中，成功训练 Navigation Networks（NavNets）来解决这个具有挑战性的部分可观察的导航任务。