  How can a delivery robot navigate reliably to a destination in a new office
building, with minimal prior information? To tackle this challenge, this paper
introduces a two-level hierarchical approach, which integrates model-free deep
learning and model-based path planning. At the low level, a neural-network
motion controller, called the intention-net, is trained end-to-end to provide
robust local navigation. The intention-net maps images from a single monocular
camera and "intentions" directly to robot controls. At the high level, a path
planner uses a crude map, e.g., a 2-D floor plan, to compute a path from the
robot's current location to the goal. The planned path provides intentions to
the intention-net. Preliminary experiments suggest that the learned motion
controller is robust against perceptual uncertainty and by integrating with a
path planner, it generalizes effectively to new environments and goals.
