VL-LN基准：面向长时程目标导航与主动对话的研究

VL-LN Bench: Towards Long-horizon Goal-oriented Navigation with Active Dialogs

December 26, 2025

作者: Wensi Huang, Shaohao Zhu, Meng Wei, Jinming Xu, Xihui Liu, Hanqing Wang, Tai Wang, Feng Zhao, Jiangmiao Pang

cs.AI

摘要

在现有的大多数具身导航任务中，指令通常被设定为明确且无歧义的，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与语言输入生成有效的导航输出。然而，现实世界的导航指令往往存在模糊性和多义性，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，我们提出交互式实例物体导航（IION）任务，该任务要求智能体不仅能生成导航动作，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。IION在实例物体导航（ION）基础上扩展，允许智能体在导航过程中以自然语言自由向信息源咨询。基于此任务，我们构建了视觉语言-语言导航（VL-LN）基准，提供大规模自动生成数据集及完整评估协议，用于训练和评估具备对话能力的导航模型。VL-LN包含超过4.1万条包含长程对话的增强轨迹用于训练，以及配备可响应智能体查询信息源的自动评估协议。利用该基准，我们训练了具备对话能力的导航模型，结果表明其性能较基线模型有显著提升。大量实验与分析进一步验证了VL-LN在推动对话式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/

English

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 Object Navigation (IION), 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. IION extends Instance Object Navigation (ION) 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/