MultINav-Bench：超越可达性的交互式导航基准与 MultINav-Agent 智能体框架

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Abstract:Existing embodied navigation tasks commonly assume that traversability and object states remain fixed during an episode. Yet reaching a goal may require opening a door to access a room and then opening a container to reveal a hidden target. We formulate interactive navigation beyond reachability, unifying channel-induced changes in spatial connectivity, container-induced changes in target visibility, and their sequential dependencies. We introduce MultINav-Bench, a 3K-episode ObjectGoal benchmark in MolmoSpaces spanning channel, container, and mixed tasks, with interaction-necessity annotations covering required and unnecessary episodes. Its data generation pipeline automates task construction across multiple task types. The evaluation protocol separately measures navigation success, interaction effectiveness, and cost. We further propose MultINav-Agent, a three-stage agentic framework built on a persistent, state-conditioned interaction graph. Semantic interactive perception grounds entities and interaction states; a vision-language model selects task-relevant actions based on expected changes in connectivity and target observability; and post-interaction verification updates the graph to guide subsequent navigation, exploration, or interaction. Experiments on MultINav-Bench show that MultINav-Agent consistently outperforms adapted baselines across channel, container, and mixed tasks.

Subjects:	Robotics (cs.RO); Artificial Intelligence (cs.AI)
ACM classes:	I.2.9
Cite as:	arXiv:2610.17089 [cs.RO]
	(or arXiv:2610.17089v1 [cs.RO] for this version)
	https://doi.org/10.48550/arXiv.2610.17089 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Delun Li [view email]
[v1] Mon, 12 Oct 2026 16:48:47 UTC (8,352 KB)