Papers › Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation
Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation
Jiannan Xiang, Xin Eric Wang, William Yang Wang
Vision-and-Language Navigation (VLN) is a natural language grounding task where an agent learns to follow language instructions and navigate to specified destinations in real-world environments. A key challenge is to recognize and stop at the correct location, especially for complicated outdoor environments. Existing methods treat the STOP action equally as other actions, which results in undesirable behaviors that the agent often fails to stop at the destination even though it might be on the right path. Therefore, we propose Learning to Stop (L2Stop), a simple yet effective policy module that differentiates STOP and other actions. Our approach achieves the new state of the art on a challenging urban VLN dataset Touchdown, outperforming the baseline by 6.89% (absolute improvement) on Success weighted by Edit Distance (SED).
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Vision and Language Navigation | Touchdown Dataset | ARC + L2STOP | Task Completion (TC) | 16.68 | #4 of 12 | Archive leaderboard | report |
| Vision and Language Navigation | Touchdown Dataset | ARC | Task Completion (TC) | 14.13 | #7 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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