# Search for or Navigate to? Dual Adaptive Thinking for Object Navigation

arXiv: https://arxiv.org/abs/2208.00553 · HTML: https://mlsanity.com/paper/2208.00553/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 49 references that are in the map.
- First posted on arXiv 1 Aug 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Embodied Question Answering](https://mlsanity.com/paper/1711.11543/) (2017)
- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [Understanding and Improving Layer Normalization](https://mlsanity.com/paper/1911.07013/) (2019)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [RoboTHOR: An Open Simulation-to-Real Embodied AI Platform](https://mlsanity.com/paper/2004.06799/) (2020)
- [PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning](https://mlsanity.com/paper/2201.10029/) (2022)
- [Visual Semantic Navigation using Scene Priors](https://mlsanity.com/paper/1810.06543/) (2018)
- [SOON: Scenario Oriented Object Navigation with Graph-based Exploration](https://mlsanity.com/paper/2103.17138/) (2021)
- [Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning](https://mlsanity.com/paper/1812.00971/) (2018)
- [Learning Object Relation Graph and Tentative Policy for Visual Navigation](https://mlsanity.com/paper/2007.11018/) (2020)
- [VTNet: Visual Transformer Network for Object Goal Navigation](https://mlsanity.com/paper/2105.09447/) (2021)
- [SSCNav: Confidence-Aware Semantic Scene Completion for Visual Semantic Navigation](https://mlsanity.com/paper/2012.04512/) (2020)
- [Continuous Scene Representations for Embodied AI](https://mlsanity.com/paper/2203.17251/) (2022)
- [Hierarchical Object-to-Zone Graph for Object Navigation](https://mlsanity.com/paper/2109.02066/) (2021)
- [Zero-shot object goal visual navigation](https://mlsanity.com/paper/2206.07423/) (2022)

## Built on by (most cited first, 20 of 20)

- [Vision-and-Language Navigation via Causal Learning](https://mlsanity.com/paper/2404.10241/) (2024)
- [CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs](https://mlsanity.com/paper/2412.10439/) (2024)
- [A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation](https://mlsanity.com/paper/2305.03602/) (2023)
- [Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation](https://mlsanity.com/paper/2302.01520/) (2023)
- [PASTS: Progress-Aware Spatio-Temporal Transformer Speaker For Vision-and-Language Navigation](https://mlsanity.com/paper/2305.11918/) (2023)
- [RATE-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models](https://mlsanity.com/paper/2506.02354/) (2025)
- [Implicit Obstacle Map-driven Indoor Navigation Model for Robust Obstacle Avoidance](https://mlsanity.com/paper/2308.12845/) (2023)
- [3DGSNav: Enhancing Vision-Language Model Reasoning for Object Navigation via Active 3D Gaussian Splatting](https://mlsanity.com/paper/2602.12159/) (2026)
- [Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation](https://mlsanity.com/paper/2312.03327/) (2023)
- [ESearch-R1: Learning Cost-Aware MLLM Agents for Interactive Embodied Search via Reinforcement Learning](https://mlsanity.com/paper/2512.18571/) (2025)
- [TrajRAG: Retrieving Geometric-Semantic Experience for Zero-Shot Object Navigation](https://mlsanity.com/paper/2605.01700/) (2026)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)
- [Causality-based Cross-Modal Representation Learning for Vision-and-Language Navigation](https://mlsanity.com/paper/2403.03405/) (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)
- [Visual Marker Search for Autonomous Drone Landing in Diverse Urban Environments](https://mlsanity.com/paper/2601.11078/) (2026)
- [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)
- [EvolveNav: Proactive Preflection and Self-Evolving Memory for Zero-Shot Object Goal Navigation](https://mlsanity.com/paper/2606.18235/) (2026)

## Abstract (arXiv)

"Search for" or "Navigate to"? When finding an object, the two choices always come up in our subconscious mind. Before seeing the target, we search for the target based on experience. After seeing the target, we remember the target location and navigate to. However, recently methods in object navigation field almost only consider using object association to enhance "search for" phase while neglect the importance of "navigate to" phase. Therefore, this paper proposes the dual adaptive thinking (DAT) method to flexibly adjust the different thinking strategies at different navigation stages. Dual thinking includes search thinking with the object association ability and navigation thinking with the target location ability. To make the navigation thinking more effective, we design the target-oriented memory graph (TOMG) to store historical target information and the target-aware multi-scale aggregator (TAMSA) to encode the relative target position. We assess our methods on the AI2-Thor dataset. Compared with the state-of-the-art (SOTA) method, our method reports 10.8%, 21.5% and 15.7% increase in success rate (SR), success weighted by path length (SPL) and success weighted by navigation efficiency (SNE), respectively.