# ReMemNav: Memory-Based Decision Correction and Target Verification for Zero-Shot Object Navigation

arXiv: https://arxiv.org/abs/2603.26788 · HTML: https://mlsanity.com/paper/2603.26788/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 36 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 28 of 28)

- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022)
- [DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames](https://mlsanity.com/paper/1911.00357/) (2019)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Recognize Anything: A Strong Image Tagging Model](https://mlsanity.com/paper/2306.03514/) (2023)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation](https://mlsanity.com/paper/2203.10421/) (2022)
- [Simple but Effective: CLIP Embeddings for Embodied AI](https://mlsanity.com/paper/2111.09888/) (2021)
- [ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings](https://mlsanity.com/paper/2206.12403/) (2022)
- [SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks](https://mlsanity.com/paper/2305.17390/) (2023)
- [InstructNav: Zero-shot System for Generic Instruction Navigation in Unexplored Environment](https://mlsanity.com/paper/2406.04882/) (2024)
- [ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation](https://mlsanity.com/paper/2301.13166/) (2023)
- [Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale](https://mlsanity.com/paper/2204.03514/) (2022)
- [SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation](https://mlsanity.com/paper/2410.08189/) (2024)
- [Bridging Zero-shot Object Navigation and Foundation Models through Pixel-Guided Navigation Skill](https://mlsanity.com/paper/2309.10309/) (2023)
- [OVRL-V2: A simple state-of-art baseline for ImageNav and ObjectNav](https://mlsanity.com/paper/2303.07798/) (2023)
- [OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation Models](https://mlsanity.com/paper/2402.10670/) (2024)
- [UniGoal: Towards Universal Zero-shot Goal-oriented Navigation](https://mlsanity.com/paper/2503.10630/) (2025)
- [How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers](https://mlsanity.com/paper/2305.16925/) (2023)
- [End-to-End Navigation with Vision Language Models: Transforming Spatial Reasoning into Question-Answering](https://mlsanity.com/paper/2411.05755/) (2024)
- [Prioritized Semantic Learning for Zero-shot Instance Navigation](https://mlsanity.com/paper/2403.11650/) (2024)
- [Learning Active Camera for Multi-Object Navigation](https://mlsanity.com/paper/2210.07505/) (2022)
- [Multi-Floor Zero-Shot Object Navigation Policy](https://mlsanity.com/paper/2409.10906/) (2024)
- [PanoNav: Mapless Zero-Shot Object Navigation with Panoramic Scene Parsing and Dynamic Memory](https://mlsanity.com/paper/2511.06840/) (2025)
- [DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation](https://mlsanity.com/paper/2505.21969/) (2025)
- [Zero-Shot Object Searching Using Large-scale Object Relationship Prior](https://mlsanity.com/paper/2303.06228/) (2023)

## Built on by (most cited first, 2 of 2)

- [Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation](https://mlsanity.com/paper/2605.27582/) (2026)
- [HAM-VLN: Harnessing Hierarchical Agentic Memory for Zero-Shot Vision-and-Language Navigation](https://mlsanity.com/paper/2607.29600/) (2026)

## Abstract (arXiv)

Zero-shot object navigation requires agents to locate unseen targets in unfamiliar environments without prior maps or task-specific training. Despite the commonsense reasoning ability of vision-language models (VLMs), existing mapless navigators often suffer from repeated exploration and premature stopping caused by limited historical context and false-positive target predictions. We propose ReMemNav, a training-free framework that combines lightweight semantic grounding, memory-based decision correction, and target verification. RAM-derived semantic priors guide panoramic direction selection, while a geometry-triggered correction mechanism retrieves historical scene descriptions when the selected direction points toward previously visited regions. When a target is predicted to be visible, ReMemNav verifies the corresponding single-view observation before the final approach. Depth-based action sampling then converts the high-level decision into collision-free motion. Experiments on HM3D and MP3D show that ReMemNav achieves higher success rates and path efficiency than existing training-free zero-shot baselines. Specifically, ReMemNav achieves absolute SR/SPL gains of 1.7%/5.3% on HM3D v0.1, 18.2%/11.1% on HM3D v0.2, and 8.7%/6.9% on MP3D.