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arXiv Computer Vision By Xiaobei Zhao, Xingqi Lyu, Xin Chen, Xiang Li

AgriVLN: Vision-and-Language Navigation for Agricultural Robots

Read the original on arXiv Computer Vision →

AgriVLN introduces a vision‑and‑language navigation benchmark (A2A) tailored for agricultural robots, comprising 1,560 episodes across six scene classes with step‑by‑step instructions and continuous actions. Existing VLN methods perform well on short instructions but poorly on long ones; to address this, the authors propose a Subtask‑Level‑Task (STL) module that decomposes complex instructions into simple subtasks. Integrating STL into the NavGPT backbone yields the AgriVLN method, improving success rate from 0.33 to 0.47 while slightly increasing navigation error from 2.76 m to 2.91 m, and the paper includes ablation and case studies on subtask quantity and scene classes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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