T2-Nav: Algebraic-Topology–Aware Temporal Graph Memory and Loop Detection for Zero-Shot Visual Navigation

Abstract

Deploying autonomous agents in the real world is complicated, especially when it comes to navigation, where systems must adapt to situations they haven’t encountered before. Traditional learning approaches require a substantial amount of data, constant tweaking, and sometimes starting over for every 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 types of inputs, employ relatively basic reasoning, and fail to fully utilize the details they observe or the structure of the spaces. Here, we introduce T2-Nav, a zero-shot navigation system that combines various types of data and employs graph-based reasoning. By leveraging visual information directly into the graph and matching it to the environment, our approach enables the system to find a good balance between exploration and reaching its goal. 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 through reference images of target object instances, making it particularly suitable for real-world deployment scenarios where agents must navigate to visually similar but spatially distinct instances. Experiments demonstrate that our approach worked efficiently and adapted well in complex, unfamiliar settings, moving toward practical zero-shot instance-image navigation capabilities.