  We present a navigation system that combines ideas from hierarchical planning
and machine learning. The system uses a traditional global planner to compute
optimal paths towards a goal, and a deep local trajectory planner and velocity
controller to compute motion commands. The latter components of the system
adjust the behavior of the robot through attention mechanisms such that it
moves towards the goal, avoids obstacles, and respects the space of nearby
pedestrians. Both the structure of the proposed deep models and the use of
attention mechanisms make the system's execution interpretable. Our simulation
experiments suggest that the proposed architecture outperforms baselines that
try to map global plan information and sensor data directly to velocity
commands. In comparison to a hand-designed traditional navigation system, the
proposed approach showed more consistent performance.
