TEA-AgriVLN: Traversability Estimation Alarm for Agricultural Vision-and-Language Navigation
Xiaobei Zhao, Xingqi Lyu, Xin Chen, Xiang Li · N/A · 2026
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Summary
Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an agent to follow a natural language instruction, predicting a sequence of low-level actions to navigate a robot from a starting point to a target location.
Abstract Summary
Key Points
- Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an agent to follow a natural language instruction, predicting a sequence of low-level actions to navigate a robot from a starting point to a target location.
- The A2A benchmark and the AgriVLN method pioneeringly extended VLN-CE from indoor scenes to agricultural scenes, while we observed a challenging distinction: In indoor scenes, whether a zone is traversable tends to be clear to classify, such as wood floors are traversable but concrete walls are not.
- In agricultural scenes, however, this issue tends to be ambiguous, such as an unripe cornfield might be traversable for a robotic dog but might be non-traversable for a human.
- To address this issue, we propose the TEA module, which estimates the traversability of the camera image, then alarm the decision-maker for rethinking when the predicted action does not align with the traversability map.
- We integrate it into the AgriVLN backbone to build our TEA-AgriVLN method
Methodology
In this section, we present our TEA-AgriVLN method, as illustrated in Figure 2 . First, we introduce the task definition in Section 3.1 . Second, we present our TEA module in Section 3.2 . Third, we integrate the TEA module into the AgriVLN backbone to build the TEA-AgriVLN method in Section 3.3 . The task of Agricultural Vision-and-Language Navigation ( Zhao et al.
Experiments
We implement all the experiments on the A2A ( Zhao et al. 2025b ) benchmark. Following the settings in A2A, we adopt GPT-4.1 as the LLM and GPT-4.1-mini as the VLM. We access all the LLMs and VLMs through APIs. Besides, we locally deploy pre-trained SAM2.1-large as the instance segmenter.
Sources and Demonstrations
Figures are reproduced from Zhao et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.
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