  Deep reinforcement learning algorithms require large and diverse datasets in
order to learn successful policies for perception-based mobile navigation.
However, gathering such datasets with a single robot can be prohibitively
expensive. Collecting data with multiple different robotic platforms with
possibly different dynamics is a more scalable approach to large-scale data
collection. But how can deep reinforcement learning algorithms leverage such
heterogeneous datasets? In this work, we propose a deep reinforcement learning
algorithm with hierarchically integrated models (HInt). At training time, HInt
learns separate perception and dynamics models, and at test time, HInt
integrates the two models in a hierarchical manner and plans actions with the
integrated model. This method of planning with hierarchically integrated models
allows the algorithm to train on datasets gathered by a variety of different
platforms, while respecting the physical capabilities of the deployment robot
at test time. Our mobile navigation experiments show that HInt outperforms
conventional hierarchical policies and single-source approaches.
