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Representations are crucial for a robot to learn effective navigationpolicies. Recent work has shown that mid-level perceptual abstractions, such asdepth estimates or 2D semantic segmentation, lead to more effective policieswhen provided as observations in place of raw sensor data (e.g., RGB images).However, such policies must still learn latent three-dimensional sceneproperties from mid-level abstractions. In contrast, high-level, hierarchicalrepresentations 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 leverageshigh-level hierarchical representations to learn navigation policies. Towardsthis goal, we propose a graph neural network architecture and show how to embeda 3D scene graph into an agent-centric feature space, which enables the robotto learn policies for low-level action in an end-to-end manner. For each nodein the scene graph, our method uses features that capture occupancy andsemantic content, while explicitly retaining memory of the robot trajectory. Wedemonstrate the effectiveness of our method against commonly used visuomotorpolicies in a challenging object search task. These experiments and supportingablation studies show that our method leads to more effective object searchbehaviors, exhibits improved long-term memory, and successfully leverageshierarchical information to guide its navigation objectives.

Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks