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中，但其移动仍严重依赖人工操作或轨道系统。AgriVLN方法与A2A基准开创性地将视觉与语言导航（VLN）拓展至农业领域，使得机器人能够根据自然语言指令导航至目标位置。然而，几乎所有现有方法均采用理想化假设——假定给出的指令本身是正确的，这与现实场景不符，因为任何指令都可能存在错误。为弥补这一缺口，我们提出A2A-MI基准，通过构建半自动数据标注器，以更高多样性和效率向每条原始指令插入三类错误。在该基准上测试若干前沿农业VLN智能体后，观察到性能显著下降：成功率（SR）降低57%，导航误差（NE）增加9%。由此推断，农业VLN智能体倾向于假定给定指令正确，因此当其所见场景与接收指令不匹配时，缺乏质疑意识。为构建对指令错误的感知能力，我们提出IMAC模块，通过分析指令与当前正面图像，判断指令是否存在错误，并尝试在必要时进行修正。将IMAC集成至基线模型后，我们观察到显著改进，有效缩小了与无错误指令性能之间的差距。项目地址：https://github.com/AlexTraveling/IMAC-AgriVLN。