T2Nav Algebraic Topology Aware Temporal Graph Memory and Loop Detection for ZeroShot Visual Navigation

2026-03-06

AI Summary

This work proposes a novel navigation system to address the limitations of existing zero-shot visual navigation methods, which often suffer from poor generalization, simplistic reasoning, and an inability to effectively leverage environmental structure and observational details. The proposed approach uniquely integrates algebraic topology-aware temporal graph memory with loop closure detection, enabling direct embedding of visual observations into a dynamically constructed temporal graph through heterogeneous data fusion. It further introduces graph-based reasoning and path planning mechanisms, supporting zero-shot navigation guided solely by target instance images. This framework achieves a balanced trade-off between efficient exploration and goal attainment in unseen environments, significantly enhancing spatial understanding, obstacle avoidance, and overall navigation robustness.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Deploying autonomous agents in real world environments is challenging, particularly for navigation, where systems must adapt to situations they have not encountered before. Traditional learning approaches require substantial amounts of data, constant tuning, and, sometimes, starting over for each new task. That makes them hard to scale and not very flexible. Recent breakthroughs in foundation models, such as large language models and vision language models, enable systems to attempt new navigation tasks without requiring additional training. However, many of these methods only work with specific input types, employ relatively basic reasoning, and fail to fully exploit the details they observe or the structure of the spaces. Here, we introduce T2Nav, a zeroshot navigation system that integrates heterogeneous data and employs graph-based reasoning. By directly incorporating visual information into the graph and matching it to the environment, our approach enables the system to strike a good balance between exploration and goal attainment. This strategy allows robust obstacle avoidance, reliable loop closure detection, and efficient path planning while eliminating redundant exploration patterns. The system demonstrates flexibility by handling goals specified using reference images of target object instances, making it particularly suitable for scenarios in which agents must navigate to visually similar yet spatially distinct instances. Experiments demonstrate that our approach is efficient and adapts well to unknown environments, moving toward practical zero-shot instance-image navigation capabilities.

Problem

• zero-shot navigation
• visual navigation
• loop closure detection
• instance-image navigation
• autonomous agents

Innovation

• zero-shot navigation
• algebraic topology
• temporal graph memory
• loop closure detection
• instance-image navigation