Navigation of the Future: Universal Zero-Shot Goal-Oriented Navigation with UniGoal

The world of Artificial Intelligence (AI) is developing rapidly, and a particularly exciting field is the navigation of robots and virtual agents. A recently published paper, accepted to CVPR 2025, introduces a promising new approach for so-called "zero-shot goal-oriented navigation": UniGoal. This framework allows agents to navigate in unknown environments and reach goals without having been specifically trained for them.

The Problem of Zero-Shot Navigation

Traditional navigation methods often require extensive training with specific data for each new environment and each new goal. This is time-consuming and resource-intensive. Zero-shot navigation, on the other hand, aims to develop agents that can adapt flexibly in new situations and achieve goals they have never seen before. Previous zero-shot methods often focused on specific tasks and goal types, limiting their general applicability.

UniGoal's Innovative Approach: A Universal Framework

UniGoal takes a novel approach by providing a universal framework for different goal types. Whether it's searching for an object of a specific category, locating a specific object based on an image, or navigating based on a text description – UniGoal can handle all these tasks. The key lies in the unified representation of scenes and goals as graphs.

Graphs as a Universal Language

Both the environment in which the agent is located and the goal are represented as graphs. The environment is represented as a "scene graph," which is updated online as the agent explores its surroundings. The goal is represented as a "goal graph," which can be structured differently depending on the goal type. This unified representation makes it possible to capture complex relationships between objects and locations and use them for navigation.

The Role of Large Language Models (LLMs)

Large Language Models (LLMs) play an important role in the UniGoal framework. They are used to draw conclusions based on the graph representations and make navigation decisions. By comparing the scene graph with the goal graph, the agent can determine which objects or locations are relevant and how to reach its goal.

Phases of Navigation

Navigation in UniGoal takes place in different phases, depending on the degree of matching between the scene and goal graphs:

If there is no match, the agent iteratively searches for subgraphs of the goal. With a partial match, the agent uses coordinate projection and anchor point alignment to estimate the target position. With a complete match, scene graph correction and target verification are performed. A blacklist mechanism ensures robust switching between the phases.

Promising Results

Experiments on various benchmarks show that UniGoal achieves excellent results compared to previous zero-shot and even some supervised methods. This suggests that the graph-based approach is a promising way to develop universal and robust navigation agents. The ability to process different goal types with a single model opens up new possibilities for the use of AI in robotics, virtual reality, and other areas.

Bibliographie: - Yin, H., Xu, X., Zhao, L., Wang, Z., Zhou, J., & Lu, J. (2025). UniGoal: Towards Universal Zero-shot Goal-oriented Navigation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). - https://huggingface.co/papers/2503.10630 - https://huggingface.co/papers - https://chatpaper.com/chatpaper/zh-CN?id=4&date=1741881600&page=1 - https://chatpaper.com/chatpaper/fr?id=4&date=1741881600&page=1 - https://arxiv.org/abs/2311.05584 - https://www.youtube.com/watch?v=Zc1vvEGRavU - https://arxiv.org/abs/2206.12403 - https://cvpr.thecvf.com/Conferences/2025/AcceptedPapers - https://www.researchgate.net/publication/372117445_Zero-Shot_Object_Goal_Visual_Navigation