外观
论文arXiv 2609.256661 个月前
为具身导航部署基础模型
原文链接：Deploying Foundation Models for Embodied Navigation（arXiv 2609.25666 · 发布日期：2026-09-22）
Deploying Foundation Models for Embodied Navigation
针对基础模型具身导航中的个性化不足与上下文受限，提出TAP和MemCtrl，并在实验室真机及多种具身任务中验证。
核心要点
- 问题在于基础模型训练偏差影响未见环境个性化，且上下文长度限制长程任务成功率。
- TAP从场景中挖掘人类习惯数据来提示模型，MemCtrl通过新增memory head主动管理记忆。
- TAP在实验室Turtlebot个性化目标寻找中较非TAP基线平均提升18%。
- MemCtrl在多种具身任务平均提升6%，长指令子集提升20%，且使用约一半基线上下文。
验证范围与局限：验证范围包括实验室环境Turtlebot真机目标寻找，以及摘要未说明具体平台的多种具身任务。
英文摘要 (Abstract)
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 limitations. We then present our approaches, Transit-Aware Planning (TAP) and MemCtrl to address the limitations. With TAP, we present real-world results in a lab environment with a Turtlebot for personalized target finding that shows an average improvement of 18% over a non-TAP baseline. On MemCtrl, we report a 6% average improvement across various embodied tasks, with 20% on long instruction subsets, all while using nearly half the context used in the baseline model. Motivated by these result, we present our stance the deployability of FM-based embodied agents in real-world environments, and highlight open research directions.
作者：Vishnu Sashank Dorbala, Dinesh Manocha
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