RWMNav: Relational World Model for Compositional Zero Shot Object Navigation

Zero shot object navigation requires an agent to leverage open vocabulary semantic knowledge to search for unseen target categories in unfamiliar environments. However, existing approaches typically represent navigation goals as a single object category, while natural language instructions in real world settings often specify the intended target instance through its relations with surrounding entities. Such relational constraints require the agent not only to recognize the target category, but also to reason about the compositional structure formed by the target, reference entities, and their relations, even before these entities become directly observable. To address this challenge, we propose RWMNav, a Relational World Model for compositional zero shot object navigation. RWMNav parses the language instruction into a relational goal graph and incrementally constructs an environmental relation graph from RGB-D observations. It then treats candidate frontiers as high level exploration actions and uses a training free relational world model instantiated with a frozen vision language model to predict the future relational states that may emerge along different exploration directions, including potential regions, entities, and their relations. The predicted relation graphs are matched with the relational goal graph to estimate goal compatibility, which is further combined with exploration utility and navigation cost for frontier selection. As exploration proceeds, predicted relational hypotheses are verified and corrected using newly acquired observations, forming a closed loop process of relational prediction, goal matching, exploration, and state update. Experiments on HM3D and MP3D demonstrate that RWMNav consistently improves relational navigation performance in terms of Relational Success Rate and Relational Success weighted by Path Length.