In most existing embodied navigation tasks, instructions are well-defined and unambiguous, such as instruction following and object searching. Under this idealized setting, agents are required solely to produce effective navigation outputs conditioned on vision and language inputs. However, real-world navigation instructions are often vague and ambiguous, requiring the agent to resolve uncertainty and infer user intent through active dialog. To address this gap, we propose Interactive Instance Goal Navigation (IIGN), a task that requires agents not only to generate navigation actions but also to produce language outputs via active dialog, thereby aligning more closely with practical settings. IIGN extends Instance Goal Navigation (IGN) by allowing agents to freely consult an oracle in natural language while navigating. Building on this task, we present the Vision Language-Language Navigation (VL-LN) benchmark, which provides a large-scale, automatically generated dataset and a comprehensive evaluation protocol for training and assessing dialog-enabled navigation models. VL-LN comprises over 41k long-horizon dialog-augmented trajectories for training and an automatic evaluation protocol with an oracle capable of responding to agent queries. Using this benchmark, we train a navigation model equipped with dialog capabilities and show that it achieves significant improvements over the baselines. Extensive experiments and analyses further demonstrate the effectiveness and reliability of VL-LN for advancing research on dialog-enabled embodied navigation. Code and dataset: https://0309hws.github.io/VL-LN.github.io/

翻译：在现有的大多数具身导航任务中，指令通常是明确且无歧义的，例如指令跟随和物体搜索。在这种理想化设定下，智能体仅需根据视觉和语言输入生成有效的导航输出。然而，现实世界中的导航指令往往是模糊且存在歧义的，要求智能体通过主动对话来消除不确定性并推断用户意图。为弥补这一差距，我们提出了交互式实例目标导航（IIGN）任务，该任务不仅要求智能体生成导航动作，还需通过主动对话产生语言输出，从而更贴近实际应用场景。IIGN 在实例目标导航（IGN）的基础上，允许智能体在导航过程中以自然语言自由向先知模块咨询。基于此任务，我们提出了视觉语言-语言导航（VL-LN）基准，该基准提供了一个大规模自动生成的数据集和一套全面的评估协议，用于训练和评估具备对话能力的导航模型。VL-LN 包含超过 41k 条用于训练的长视野对话增强轨迹，以及一套配备可响应智能体查询的先知模块的自动评估协议。利用该基准，我们训练了一个具备对话能力的导航模型，并证明其在多项基线模型上取得了显著提升。大量实验与分析进一步验证了 VL-LN 在推动具备对话能力的具身导航研究方面的有效性和可靠性。代码与数据集：https://0309hws.github.io/VL-LN.github.io/