让农业机器人更懂复杂指令，提升导航准确率。
T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
- 引入指令翻译模块，修正语言指令中的噪声与错误。
- 在A2A基准上成功率从47%提升至63%，导航误差降至2.28米。
- 适合农业机器人、多模态导航研究者参考使用。
农业机器人在各类农事任务中日益成为得力助手，但仍严重依赖人工操作或固定轨道移动。为突破此限制，AgriVLN方法与A2A基准首次将视觉-语言导航（VLN）拓展至农业领域，使机器人能根据自然语言指令导航至目标位置。我们发现，AgriVLN能有效理解简单指令，但常误解复杂指令。为此，我们提出T-araVLN方法，构建指令翻译模块，将有噪声和错误的指令转化为精确表示。在A2A基准上评估，T-araVLN将成功率（SR）从0.47提升至0.63，导航误差（NE）从2.91米降至2.28米，实现农业视觉-语言导航领域的最先进性能。代码已开源：https://github.com/AlexTraveling/T-araVLN。
原文摘要 · Abstract (English)
Agricultural robotic agents have been becoming useful helpers in a wide range of agricultural tasks. However, they still heavily rely on manual operations or fixed railways for movement. To address this limitation, the AgriVLN method and the A2A benchmark pioneeringly extend Vision-and-Language Navigation (VLN) to the agricultural domain, enabling agents to navigate to the target positions following the natural language instructions. We observe that AgriVLN can effectively understands the simple instructions, but often misunderstands the complex ones. To bridge this gap, we propose the T-araVLN method, in which we build the instruction translator module to translate noisy and mistaken instructions into refined and precise representations. When evaluated on A2A, our T-araVLN successfully improves Success Rate (SR) from 0.47 to 0.63 and reduces Navigation Error (NE) from 2.91m to 2.28m, demonstrating the state-of-the-art performance in the agricultural VLN domain. Code: https://github.com/AlexTraveling/T-araVLN.
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