  This work presents a modular and hierarchical approach to learn policies for
exploring 3D environments, called `Active Neural SLAM'. Our approach leverages
the strengths of both classical and learning-based methods, by using analytical
path planners with learned SLAM module, and global and local policies. The use
of learning provides flexibility with respect to input modalities (in the SLAM
module), leverages structural regularities of the world (in global policies),
and provides robustness to errors in state estimation (in local policies). Such
use of learning within each module retains its benefits, while at the same
time, hierarchical decomposition and modular training allow us to sidestep the
high sample complexities associated with training end-to-end policies. Our
experiments in visually and physically realistic simulated 3D environments
demonstrate the effectiveness of our approach over past learning and
geometry-based approaches. The proposed model can also be easily transferred to
the PointGoal task and was the winning entry of the CVPR 2019 Habitat PointGoal
Navigation Challenge.
