# Hierarchical Object-to-Zone Graph for Object Navigation

arXiv: https://arxiv.org/abs/2109.02066 · HTML: https://mlsanity.com/paper/2109.02066/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 45 references that are in the map.
- First posted on arXiv 5 Sep 2021.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [Semi-parametric Topological Memory for Navigation](https://mlsanity.com/paper/1803.00653/) (2018)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [Neural Topological SLAM for Visual Navigation](https://mlsanity.com/paper/2005.12256/) (2020)
- [RoboTHOR: An Open Simulation-to-Real Embodied AI Platform](https://mlsanity.com/paper/2004.06799/) (2020)
- [Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs](https://mlsanity.com/paper/1803.08035/) (2018)
- [Visual Semantic Navigation using Scene Priors](https://mlsanity.com/paper/1810.06543/) (2018)
- [Learning Exploration Policies for Navigation](https://mlsanity.com/paper/1903.01959/) (2019)
- [Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks](https://mlsanity.com/paper/1903.03878/) (2019)
- [Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning](https://mlsanity.com/paper/1812.00971/) (2018)
- [Visual Representations for Semantic Target Driven Navigation](https://mlsanity.com/paper/1805.06066/) (2018)
- [Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation](https://mlsanity.com/paper/1709.10489/) (2017)
- [Learning Object Relation Graph and Tentative Policy for Visual Navigation](https://mlsanity.com/paper/2007.11018/) (2020)
- [The More You Know: Using Knowledge Graphs for Image Classification](https://mlsanity.com/paper/1612.04844/) (2016)
- [Combining Optimal Control and Learning for Visual Navigation in Novel Environments](https://mlsanity.com/paper/1903.02531/) (2019)
- [Bayesian Relational Memory for Semantic Visual Navigation](https://mlsanity.com/paper/1909.04306/) (2019)
- [A Behavioral Approach to Visual Navigation with Graph Localization Networks](https://mlsanity.com/paper/1903.00445/) (2019)

## Built on by (most cited first, 30 of 34)

- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning](https://mlsanity.com/paper/2201.10029/) (2022)
- [GridMM: Grid Memory Map for Vision-and-Language Navigation](https://mlsanity.com/paper/2307.12907/) (2023)
- [SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation](https://mlsanity.com/paper/2410.08189/) (2024)
- [Lookahead Exploration with Neural Radiance Representation for Continuous Vision-Language Navigation](https://mlsanity.com/paper/2404.01943/) (2024)
- [How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers](https://mlsanity.com/paper/2305.16925/) (2023)
- [Sim-to-Real Transfer via 3D Feature Fields for Vision-and-Language Navigation](https://mlsanity.com/paper/2406.09798/) (2024)
- [g3D-LF: Generalizable 3D-Language Feature Fields for Embodied Tasks](https://mlsanity.com/paper/2411.17030/) (2024)
- [Search for or Navigate to? Dual Adaptive Thinking for Object Navigation](https://mlsanity.com/paper/2208.00553/) (2022)
- [Unbiased Directed Object Attention Graph for Object Navigation](https://mlsanity.com/paper/2204.04421/) (2022)
- [Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation](https://mlsanity.com/paper/2402.17587/) (2024)
- [Graph based Environment Representation for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2301.04352/) (2023)
- [Aligning Knowledge Graph with Visual Perception for Object-goal Navigation](https://mlsanity.com/paper/2402.18892/) (2024)
- [ForesightNav: Learning Scene Imagination for Efficient Exploration](https://mlsanity.com/paper/2504.16062/) (2025)
- [Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation](https://mlsanity.com/paper/2302.01520/) (2023)
- [EfficientNav: Towards On-Device Object-Goal Navigation with Navigation Map Caching and Retrieval](https://mlsanity.com/paper/2510.18546/) (2025)
- [Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation](https://mlsanity.com/paper/2208.13031/) (2022)
- [Implicit Obstacle Map-driven Indoor Navigation Model for Robust Obstacle Avoidance](https://mlsanity.com/paper/2308.12845/) (2023)
- [Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation](https://mlsanity.com/paper/2312.03327/) (2023)
- [GA-VLN: Geometry-Aware BEV Representation for Efficient Vision-Language Navigation](https://mlsanity.com/paper/2605.22036/) (2026)
- [PSG-Nav: Probabilistic Scene Graph Navigation via Multiverse Decision Making](https://mlsanity.com/paper/2606.01313/) (2026)
- [Symmetry-aware Neural Architecture for Embodied Visual Navigation](https://mlsanity.com/paper/2112.09515/) (2021)
- [Unsupervised Visual Odometry and Action Integration for PointGoal Navigation in Indoor Environment](https://mlsanity.com/paper/2210.00413/) (2022)
- [Task-Driven Graph Attention for Hierarchical Relational Object Navigation](https://mlsanity.com/paper/2306.13760/) (2023)
- [Learning to Terminate in Object Navigation](https://mlsanity.com/paper/2309.16164/) (2023)
- [RobKiNet: Robotic Kinematics Informed Neural Network for Optimal Robot Configuration Prediction](https://mlsanity.com/paper/2402.16281/) (2024)
- [Temporal-Spatial Object Relations Modeling for Vision-and-Language Navigation](https://mlsanity.com/paper/2403.15691/) (2024)
- [Leveraging Unknown Objects to Construct Labeled-Unlabeled Meta-Relationships for Zero-Shot Object Navigation](https://mlsanity.com/paper/2405.15222/) (2024)
- [Efficient Strategy Learning by Decoupling Searching and Pathfinding for Object Navigation](https://mlsanity.com/paper/2406.14103/) (2024)
- [Semantic Environment Atlas for Object-Goal Navigation](https://mlsanity.com/paper/2410.09081/) (2024)

## Abstract (arXiv)

The goal of object navigation is to reach the expected objects according to visual information in the unseen environments. Previous works usually implement deep models to train an agent to predict actions in real-time. However, in the unseen environment, when the target object is not in egocentric view, the agent may not be able to make wise decisions due to the lack of guidance. In this paper, we propose a hierarchical object-to-zone (HOZ) graph to guide the agent in a coarse-to-fine manner, and an online-learning mechanism is also proposed to update HOZ according to the real-time observation in new environments. In particular, the HOZ graph is composed of scene nodes, zone nodes and object nodes. With the pre-learned HOZ graph, the real-time observation and the target goal, the agent can constantly plan an optimal path from zone to zone. In the estimated path, the next potential zone is regarded as sub-goal, which is also fed into the deep reinforcement learning model for action prediction. Our methods are evaluated on the AI2-Thor simulator. In addition to widely used evaluation metrics SR and SPL, we also propose a new evaluation metric of SAE that focuses on the effective action rate. Experimental results demonstrate the effectiveness and efficiency of our proposed method.