SOURCE-LINKED INTELLIGENCE
Deploying Foundation Models for Embodied Navigation
We present and tackle two problems associated with deploying Foundation Models (FMs) on Embodied Agents performing navigation: 1) Training bias in FMs leading to poor personalization in unseen environments, and 2) Limited FM context length hindering success, especially on long horizon tasks. Our solution for the former involves priming the FM with human-habit data mined from the scene and our solution for the latter involves active memory management via a novel `memory head' augmentation. We first present a taxonomy of existing literature on FM-based Embodied Navigation, and highlight these li
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-22T04:16:59.000Z
First collected: 2026-09-23T04:21:13.910Z. This is not the publication date.