RAGNav: A Retrieval-Augmented Topological Reasoning Framework for Multi-Goal Visual-Language Navigation
arXiv:2603.03745v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) is evolving from single-point pathfinding toward the more challenging Multi-Goal VLN. This task requires agents to accurately identify multiple entities while collaboratively reasoning over their spatial-physical constraints and sequential execution order. However, generic Retrieval-Augmented Generation (RAG) paradigms often suffer from spatial hallucinations and planning drift when handling multi-object associations due to the lack of explicit spatial modeling.To address these challenges, we propose RAGNav, a framework that bridges the gap between semantic reasoning and physical structure. The core of RAGNav is a Dual-Basis Memory system, which integrates a low-level topological map for maintaining physical connectivity with a high-level semantic forest for hierarchical environment abstraction. Building on this representation, the framework introduces an anchor-guided conditional retrieval and a topologi
arXiv:2603.03745v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) is evolving from single-point pathfinding toward the more challenging Multi-Goal VLN. This task requires agents to accurately identify multiple entities while collaboratively reasoning over their spatial-physical constraints and sequential execution order. However, generic Retrieval-Augmented Generation (RAG) paradigms often suffer from spatial hallucinations and planning drift when handling multi-object associations due to the lack of explicit spatial modeling.To address these challenges, we propose RAGNav, a framework that bridges the gap between semantic reasoning and physical structure. The core of RAGNav is a Dual-Basis Memory system, which integrates a low-level topological map for maintaining physical connectivity with a high-level semantic forest for hierarchical environment abstraction. Building on this representation, the framework introduces an anchor-guided conditional retrieval and a topological neighbor score propagation mechanism. This approach facilitates the rapid screening of candidate targets and the elimination of semantic noise, while performing semantic calibration by leveraging the physical associations inherent in the topological neighborhood.This mechanism significantly enhances the capability of inter-target reachability reasoning and the efficiency of sequential planning. Experimental results demonstrate that RAGNav achieves state-of-the-art (SOTA) performance in complex multi-goal navigation tasks.
Executive Summary
This article proposes RAGNav, a novel framework addressing challenges in Multi-Goal Visual-Language Navigation (VLN) by leveraging a Dual-Basis Memory system and anchor-guided conditional retrieval. The framework integrates a low-level topological map with a high-level semantic forest to facilitate rapid screening of candidate targets, elimination of semantic noise, and semantic calibration. Experimental results demonstrate state-of-the-art performance in complex multi-goal navigation tasks. RAGNav effectively bridges the gap between semantic reasoning and physical structure, enabling more accurate and efficient navigation through environments with multiple objects and constraints.
Key Points
- ▸ RAGNav leverages a Dual-Basis Memory system to address challenges in Multi-Goal VLN
- ▸ The framework integrates a low-level topological map with a high-level semantic forest for hierarchical environment abstraction
- ▸ Anchor-guided conditional retrieval and topological neighbor score propagation are introduced to facilitate rapid target screening and semantic calibration
Merits
Semantic Calibration and Noise Elimination
RAGNav's topological neighbor score propagation mechanism enables rapid screening of candidate targets and elimination of semantic noise, leading to more accurate navigation
Efficient Sequential Planning
The framework's anchor-guided conditional retrieval facilitates efficient sequential planning and semantic calibration, enhancing inter-target reachability reasoning
State-of-the-Art Performance
Experimental results demonstrate RAGNav's state-of-the-art performance in complex multi-goal navigation tasks, indicating its potential for real-world applications
Demerits
Complexity and Computational Requirements
The Dual-Basis Memory system and anchor-guided conditional retrieval mechanisms may introduce additional computational complexity, potentially limiting the framework's applicability to resource-constrained environments
Limited Generalizability
The framework's performance may be specific to the task and environment used in the experimental evaluation, and its generalizability to other scenarios is uncertain
Expert Commentary
RAGNav represents a significant advancement in the field of Visual-Language Navigation, addressing key challenges in multi-goal navigation and semantic reasoning. The framework's Dual-Basis Memory system and anchor-guided conditional retrieval mechanisms demonstrate a unique ability to bridge the gap between semantic and physical structure. While the framework's complexity and computational requirements are potential limitations, its state-of-the-art performance and potential applications in AI-powered robots and agents make it a significant contribution to the field.
Recommendations
- ✓ Future research should focus on evaluating RAGNav's performance in a wider range of scenarios and environments to assess its generalizability
- ✓ The framework's complexity and computational requirements should be further optimized to improve its applicability to resource-constrained environments