# Zero-shot object goal visual navigation

arXiv: https://arxiv.org/abs/2206.07423 · HTML: https://mlsanity.com/paper/2206.07423/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [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)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Open-Vocabulary Object Detection Using Captions](https://mlsanity.com/paper/2011.10678/) (2020)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Speaker-Follower Models for Vision-and-Language Navigation](https://mlsanity.com/paper/1806.02724/) (2018)
- [Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/1906.00817/) (2019)
- [Simple but Effective: CLIP Embeddings for Embodied AI](https://mlsanity.com/paper/2111.09888/) (2021)
- [Visual Semantic Navigation using Scene Priors](https://mlsanity.com/paper/1810.06543/) (2018)
- [Generalized Zero-Shot Learning via Synthesized Examples](https://mlsanity.com/paper/1712.03878/) (2017)
- [Learning Graph Embeddings for Compositional Zero-shot Learning](https://mlsanity.com/paper/2102.01987/) (2021)
- [Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning](https://mlsanity.com/paper/1812.00971/) (2018)
- [Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation](https://mlsanity.com/paper/1903.02547/) (2019)
- [VTNet: Visual Transformer Network for Object Goal Navigation](https://mlsanity.com/paper/2105.09447/) (2021)
- [Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts](https://mlsanity.com/paper/1803.06049/) (2018)
- [Synthesizing the Unseen for Zero-shot Object Detection](https://mlsanity.com/paper/2010.09425/) (2020)
- [Benchmarking Classic and Learned Navigation in Complex 3D Environments](https://mlsanity.com/paper/1901.10915/) (2019)
- [Polarity Loss for Zero-shot Object Detection](https://mlsanity.com/paper/1811.08982/) (2018)
- [To Learn or Not to Learn: Analyzing the Role of Learning for Navigation in Virtual Environments](https://mlsanity.com/paper/1907.11770/) (2019)

## Built on by (most cited first, 30 of 31)

- [Large Language Models for Robotics: A Survey](https://mlsanity.com/paper/2311.07226/) (2023)
- [VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model](https://mlsanity.com/paper/2401.02695/) (2024)
- [UAV-ON: A Benchmark for Open-World Object Goal Navigation with Aerial Agents](https://mlsanity.com/paper/2508.00288/) (2025)
- [Search for or Navigate to? Dual Adaptive Thinking for Object Navigation](https://mlsanity.com/paper/2208.00553/) (2022)
- [Zero-shot Object Navigation with Vision-Language Models Reasoning](https://mlsanity.com/paper/2410.18570/) (2024)
- [Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation](https://mlsanity.com/paper/2309.08138/) (2023)
- [Evidential Active Recognition: Intelligent and Prudent Open-World Embodied Perception](https://mlsanity.com/paper/2311.13793/) (2023)
- [PanoNav: Mapless Zero-Shot Object Navigation with Panoramic Scene Parsing and Dynamic Memory](https://mlsanity.com/paper/2511.06840/) (2025)
- [Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation](https://mlsanity.com/paper/2302.01520/) (2023)
- [LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation](https://mlsanity.com/paper/2405.05363/) (2024)
- [Zero-shot Active Visual Search (ZAVIS): Intelligent Object Search for Robotic Assistants](https://mlsanity.com/paper/2209.08803/) (2022)
- [TDANet: Target-Directed Attention Network For Object-Goal Visual Navigation With Zero-Shot Ability](https://mlsanity.com/paper/2404.08353/) (2024)
- [CAMON: Cooperative Agents for Multi-Object Navigation with LLM-based Conversations](https://mlsanity.com/paper/2407.00632/) (2024)
- [ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment](https://mlsanity.com/paper/2503.02505/) (2025)
- [CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking](https://mlsanity.com/paper/2507.11334/) (2025)
- [TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals](https://mlsanity.com/paper/2509.08699/) (2025)
- [Active Open-Vocabulary Recognition: Let Intelligent Moving Mitigate CLIP Limitations](https://mlsanity.com/paper/2311.17938/) (2023)
- [Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents](https://mlsanity.com/paper/2412.11484/) (2024)
- [Space-Aware Instruction Tuning: Dataset and Benchmark for Guide Dog Robots Assisting the Visually Impaired](https://mlsanity.com/paper/2502.07183/) (2025)
- [ObjectReact: Learning Object-Relative Control for Visual Navigation](https://mlsanity.com/paper/2509.09594/) (2025)
- [Weakly-supervised VLM-guided Partial Contrastive Learning for Visual Language Navigation](https://mlsanity.com/paper/2506.15757/) (2025)
- [Language-Based Augmentation to Address Shortcut Learning in Object Goal Navigation](https://mlsanity.com/paper/2402.05090/) (2024)
- [Diffusion as Reasoning: Enhancing Object Navigation via Diffusion Model Conditioned on LLM-based Object-Room Knowledge](https://mlsanity.com/paper/2410.21842/) (2024)
- [Efficient and Generalizable Environmental Understanding for Visual Navigation](https://mlsanity.com/paper/2506.15377/) (2025)
- [Schr"odinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation](https://mlsanity.com/paper/2512.21201/) (2025)
- [Zero-Shot Object Goal Visual Navigation With Class-Independent Relationship Network](https://mlsanity.com/paper/2310.09883/) (2023)
- [Leveraging Unknown Objects to Construct Labeled-Unlabeled Meta-Relationships for Zero-Shot Object Navigation](https://mlsanity.com/paper/2405.15222/) (2024)
- [Reliable Semantic Understanding for Real World Zero-shot Object Goal Navigation](https://mlsanity.com/paper/2410.21926/) (2024)
- [AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning](https://mlsanity.com/paper/2601.15614/) (2026)
- [SR-Nav: Spatial Relationships Matter for Zero-shot Object Goal Navigation](https://mlsanity.com/paper/2603.18443/) (2026)

## Abstract (arXiv)

Object goal visual navigation is a challenging task that aims to guide a robot to find the target object based on its visual observation, and the target is limited to the classes pre-defined in the training stage. However, in real households, there may exist numerous target classes that the robot needs to deal with, and it is hard for all of these classes to be contained in the training stage. To address this challenge, we study the zero-shot object goal visual navigation task, which aims at guiding robots to find targets belonging to novel classes without any training samples. To this end, we also propose a novel zero-shot object navigation framework called semantic similarity network (SSNet). Our framework use the detection results and the cosine similarity between semantic word embeddings as input. Such type of input data has a weak correlation with classes and thus our framework has the ability to generalize the policy to novel classes. Extensive experiments on the AI2-THOR platform show that our model outperforms the baseline models in the zero-shot object navigation task, which proves the generalization ability of our model. Our code is available at: https://github.com/pioneer-innovation/Zero-Shot-Object-Navigation.