  Representations are crucial for a robot to learn effective navigation
policies. Recent work has shown that mid-level perceptual abstractions, such as
depth estimates or 2D semantic segmentation, lead to more effective policies
when provided as observations in place of raw sensor data (e.g., RGB images).
However, such policies must still learn latent three-dimensional scene
properties from mid-level abstractions. In contrast, high-level, hierarchical
representations such as 3D scene graphs explicitly provide a scene's geometry,
topology, and semantics, making them compelling representations for navigation.
In this work, we present a reinforcement learning framework that leverages
high-level hierarchical representations to learn navigation policies. Towards
this goal, we propose a graph neural network architecture and show how to embed
a 3D scene graph into an agent-centric feature space, which enables the robot
to learn policies for low-level action in an end-to-end manner. For each node
in the scene graph, our method uses features that capture occupancy and
semantic content, while explicitly retaining memory of the robot trajectory. We
demonstrate the effectiveness of our method against commonly used visuomotor
policies in a challenging object search task. These experiments and supporting
ablation studies show that our method leads to more effective object search
behaviors, exhibits improved long-term memory, and successfully leverages
hierarchical information to guide its navigation objectives.
