Path Mixing VLN dataset

The Room -to-Room (R2R) dataset consists of human annotated instructions corresponding to the paths in these graphs. Each path consists of a sequence of viewpoints encountered by the agent during navigation. A derived dataset, Fine-Grained R2R (FGR2R) dataset, annotated parts of instructions with corresponding graph edges to obtain a fine-grained dataset. Existing works in VLN have shown that more instruction examples can improve an agent’s performance in previously unseen environments. We generate 162k instruction-trajectory pairs with path lengths between 5m and 20m. The final dataset has on average 7.27 views per path, a mean of 14.4m trajectory length and an average of 82 words per instruction.房间到房间（Room-to-Room，R2R）数据集包含与这些图中路径相对应的人工标注导航指令。每条路径均由智能体在导航过程中途经的一系列视点序列构成。衍生数据集细粒度R2R（Fine-Grained R2R，FGR2R）数据集则通过将指令片段与对应的图边进行关联标注，构建得到细粒度版本数据集。视觉语言导航（Visual Language Navigation, VLN）领域现有研究已证实，更多的导航指令样本能够提升智能体在未知环境中的导航性能。我们生成了16.2万条指令-轨迹对，对应路径长度介于5米至20米之间。最终数据集的各项统计指标为：平均每条路径对应7.27个视点，轨迹平均长度达14.4米，每条指令平均包含82个词汇。