[Submitted on 28 Sep 2026]
Title:Systematic Multi-Agent Vision-and-Language Navigation: Formulation, Benchmark, and Method
View a PDF of the paper titled Systematic Multi-Agent Vision-and-Language Navigation: Formulation, Benchmark, and Method, by Yunzhe Xu and Zhe Liu
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Abstract:Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require teams of robots to tackle tasks beyond the capabilities of any individual agent. We present Systematic Multi-Agent Vision-and-Language Navigation, providing, to our knowledge, the first systematic formalization of multi-agent VLN as a constrained coordination problem: each mission consists of subtasks carrying dependency and resource constraints (presence locks and holding chains). A verified four-stage crafting pipeline instantiates the task as MAVLN, comprising 11,724 episodes across 145 scenes with teams of up to four agents under three instruction regimes, accompanied by tailored constraint-aware metrics. We further present TRISS, a coordination-ready navigation system coupling an LLM-based subtask scheduler, a shared topological memory that turns each agent's exploration into team knowledge, and a conflict-aware execution mechanism that realizes simultaneous intentions as collision-free routes. Extensive experiments establish TRISS as a comprehensive baseline and reveal substantial room for improvement across scheduling, planning, and execution, highlighting the challenges of coordinating under MAVLN task constraints. Project page: this https URL.
Comments: 39 pages, 18 figures, 16 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2609.35965 [cs.CV]
(or arXiv:2609.35965v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.35965
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yunzhe Xu [view email] [v1] Mon, 28 Sep 2026 18:00:01 UTC (8,672 KB)
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