Agricultural robots are serving as powerful assistants across a wide range of agricultural tasks, nevertheless, still heavily relying on manual operations or railway systems for movement. The AgriVLN method and the A2A benchmark pioneeringly extended Vision-and-Language Navigation (VLN) to the agricultural domain, enabling a robot to navigate to a target position following a natural language instruction. However, almost all the prior methods adopt an ideal assumption that the given instructions themselves are correct, which does not align with the realistic scenarios, because anybody may say an instruction with mistakes. To bridge this gap, we propose the A2A-MI benchmark, in which we build a semi-automatic data annotator to insert three mistake classifications into each original instruction in a more diversified and efficient way. We test several state-of-the-art agricultural VLN agents on it and observe a sufficient drop with -57% on SR and -9% on NE, from which we suggest that an agricultural VLN agent tends to assume that the given instruction is correct, so does not have the awareness to doubt it when the scenes it sees do not align with the instruction it receives. To build the awareness on instruction mistake, we propose the IMAC module analyzing the instruction and the current front-facing image, to judge whether the instruction has mistakes and attempt to correct it when needed. We integrate IMAC into the baseline model, and observe a noteworthy improvement, sufficiently narrowing the gap to the performance on instructions without mistakes. Project: https://github.com/AlexTraveling/IMAC-AgriVLN.

翻译：农业机器人正作为强大助手广泛应用于各类农业任务，但仍严重依赖人工操作或轨道系统实现移动。AgriVLN方法与A2A基准率先将视觉语言导航（VLN）拓展至农业领域，使机器人能够根据自然语言指令导航至目标位置。然而，几乎所有现有方法都采用理想假设——给定指令本身正确无误，这与现实场景不符，因为任何人都可能说出包含错误的指令。为弥补这一差距，我们提出A2A-MI基准，构建了半自动数据标注器，以更多样化和高效的方式在每个原始指令中插入三类错误。在此基准上测试了多种最先进的农业VLN智能体，观察到性能显著下降（成功率降57%，导航误差升9%），由此推断：农业VLN智能体倾向于假定给定指令正确，因此在看到场景与接收指令不一致时缺乏质疑意识。为建立指令错误察觉能力，我们提出IMAC模块，通过分析指令与当前前置图像，判断指令是否存在错误并在必要时尝试修正。将IMAC集成至基线模型后，观察到显著改进，充分缩小了与无错误指令场景下的性能差距。项目链接：https://github.com/AlexTraveling/IMAC-AgriVLN。