摘要：视觉与语言导航（Vision-and-Language Navigation，VLN）研究长期以来主要聚焦于单个智能体遵循单条指令执行导航任务，但许多现实应用需要机器人团队完成超出任一单独智能体能力范围的任务。我们提出系统化多智能体视觉与语言导航，据我们所知，首次将多智能体 VLN 系统化地形式化为一个受约束的协同问题：每项任务由携带依赖关系和资源约束（在位锁定与持有链）的子任务构成。通过一套经过验证的四阶段构建流程，我们将该任务实例化为 MAVLN；该基准包含 145 个场景中的 11,724 个任务回合，团队规模最多为四个智能体，覆盖三种指令设定，并配备定制的约束感知指标。我们进一步提出 TRISS，一个面向协同的导航系统，结合了基于 LLM 的子任务调度器、将每个智能体的探索转化为团队知识的共享拓扑记忆，以及将同步意图实现为无碰撞路径的冲突感知执行机制。大量实验将 TRISS 确立为一个综合性基线，并表明在调度、规划和执行方面仍有很大的改进空间，凸显了在 MAVLN 任务约束下进行协同所面临的挑战。项目页面：此 HTTPS 链接。

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.