Embodied AI agents in large scenes often need to navigate to find objects. In this work, we study a naturally emerging variant of the object navigation task, hierarchical relational object navigation (HRON), where the goal is to find objects specified by logical predicates organized in a hierarchical structure - objects related to furniture and then to rooms - such as finding an apple on top of a table in the kitchen. Solving such a task requires an efficient representation to reason about object relations and correlate the relations in the environment and in the task goal. HRON in large scenes (e.g. homes) is particularly challenging due to its partial observability and long horizon, which invites solutions that can compactly store the past information while effectively exploring the scene. We demonstrate experimentally that scene graphs are the best-suited representation compared to conventional representations such as images or 2D maps. We propose a solution that uses scene graphs as part of its input and integrates graph neural networks as its backbone, with an integrated task-driven attention mechanism, and demonstrate its better scalability and learning efficiency than state-of-the-art baselines.
@article{arxiv.2306.13760,
title = {Task-Driven Graph Attention for Hierarchical Relational Object Navigation},
author = {Michael Lingelbach and Chengshu Li and Minjune Hwang and Andrey Kurenkov and Alan Lou and Roberto Martín-Martín and Ruohan Zhang and Li Fei-Fei and Jiajun Wu},
journal= {arXiv preprint arXiv:2306.13760},
year = {2023}
}