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Evidence record 312 · automatically gathered

CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation

Vision-Language Navigation has increasingly emphasized high-level instruction reasoning, memory, global map construction, and instruction decomposition, while the low-level action representation remains comparatively underexplored. We propose CoFL-S, a low-level vision-language-action framework that predicts a language-conditioned flow field over the robot's local visible sector and generates continuous trajectories by rolling out the predicted field. To train this low-level representation, we c

Record details

Published: 2 July 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy
Retrieved: 14 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (2 July 2026), “CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation,” evidence record 312, https://ethics.ai/record/312 (originally published by arXiv).

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