标题： 使用大型视觉-语言模型遵循路线指示：低级和全景动作空间的比较 显示英文标题

标题： Following Route Instructions using Large Vision-Language Models: A Comparison between Low-level and Panoramic Action Spaces

摘要： 视觉与语言导航（VLN）是指使自主机器人通过遵循自然语言指令来导航不熟悉的环境的任务。 尽管最近的大型视觉-语言模型（LVLMs）在这个任务中显示出潜力，但大多数当前的VLM系统依赖于专门设计和优化的模型进行导航，使得现成的LVLMs的潜力尚未得到充分探索。 此外，虽然较早的VLN方法使用了以自我为中心视角和原子动作（如“向左转”或“向前移动”）的低级动作空间，但较新的模型倾向于选择具有离散可导航视点的全景动作空间。 本文研究了（1）现成的LVLMs（未经架构修改或基于模拟器的训练微调）是否能有效支持VLN任务，以及（2）这些模型是否能支持低级和全景动作范式。 为此，我们在Room-to-Room（R2R）数据集上对开源模型Qwen2.5-VL-3B-Instruct进行微调，并在其低级和全景动作空间中评估其经验性能。 最佳结果模型在R2R测试集上实现了41%的成功率，这表明虽然现成的LVLMs可以学习执行视觉与语言导航，但它们仍落后于专门为该任务设计的模型。 显示英文摘要

摘要： Vision-and-Language Navigation (VLN) refers to the task of enabling autonomous robots to navigate unfamiliar environments by following natural language instructions. While recent Large Vision-Language Models (LVLMs) have shown promise in this task, most current VLM systems rely on models specifically designed and optimized for navigation, leaving the potential of off-the-shelf LVLMs underexplored. Furthermore, while older VLN approaches used low-level action spaces with egocentric views and atomic actions (such as "turn left" or "move forward"), newer models tend to favor panoramic action spaces with discrete navigable viewpoints. This paper investigates (1) whether off-the-shelf LVLMs (fine-tuned without architectural modifications or simulator-based training) can effectively support VLN tasks and (2) whether such models can support both low-level and panoramic action paradigms. To this end, we fine-tune the open-source model Qwen2.5-VL-3B-Instruct on the Room-to-Room (R2R) dataset and evaluate its empirical performance across both low-level and panoramic action spaces. The best resulting model achieves a 41% success rate on the R2R test set, demonstrating that while off-the-shelf LVLMs can learn to perform Vision-and-Language Navigation, they still lag behind models specifically designed for this task.