# InsightMap：用于具身多模态推理的结构化空间建模

> 原文链接：https://robotics.hellobike.com/papers/2026-09-29-2609-37187
> 频道：papers | 更新日期：2026-09-29 | 标签：arxiv, cs.RO
> 核心摘要：InsightMap以俯视地图作显式空间记忆和动作条件预测目标，在导航、问答、推理、3D定位及Unitree Go2上验证。

# InsightMap：用于具身多模态推理的结构化空间建模

::: v-pre
> 原文链接：[InsightMap: Structured Spatial Modeling for Embodied Multimodal Reasoning](https://arxiv.org/abs/2609.37187)（arXiv 2609.37187 · 发布日期：2026-09-29）

**InsightMap: Structured Spatial Modeling for Embodied Multimodal Reasoning**

InsightMap以俯视地图作显式空间记忆和动作条件预测目标，在导航、问答、推理、3D定位及Unitree Go2上验证。

## 核心要点

- 语言引导导航需将局部观测连接到持久空间参照，并学习动作如何改变表示。
- 框架把历史视角关联到带标签地图位置，共享多模态骨干联合学习动作预测与后动作地图生成。
- R2R-CE/RxR-CE验证未见划分SR为56.9%/54.9%，地图监督提升R2R-CE表现。
- 静态空间任务报告ScanQA、SQA3D和ScanRefer指标；Unitree Go2上优于NaVid和NaVILA。

**验证范围与局限**：验证包括R2R-CE与RxR-CE验证未见划分、静态空间任务，以及Unitree Go2在走廊、实验室和办公室环境中的测试。

## 英文摘要 (Abstract)

Language-guided navigation requires connecting partial observations to a persistent spatial reference and learning how actions change that representation. We introduce InsightMap, a framework that uses top-down maps as both explicit spatial memory and action-conditioned prediction targets. Historical views are linked to labeled map locations, and a shared multimodal backbone jointly learns navigation action prediction and post-action map generation. Map prediction provides auxiliary training supervision, while navigation inference decodes actions from the observed spatial context. An aligned RGB-D data pipeline supports a common interface for navigation, visual question answering, situated reasoning, and 3D grounding. On the validation-unseen splits of R2R-CE and RxR-CE, InsightMap achieves success rates (SR) of 56.9% and 54.9%, respectively. Adding map-prediction supervision improves R2R-CE SR by 4.3 and success weighted by path length (SPL) by 3.2 percentage points. On static spatial tasks, InsightMap achieves 103.7 CIDEr on ScanQA, 60.1% exact-match accuracy on SQA3D, and 53.1% grounding accuracy at 0.5 IoU on ScanRefer with detected object proposals. On Unitree Go2, it outperforms NaVid and NaVILA in hallway, lab, and office environments.

作者：Hongpei Zheng, Hujun Yin
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## 原始参考来源 (Sources)

- [arXiv 2609.37187：InsightMap: Structured Spatial Modeling for Embodied Multimodal Reasoning](https://arxiv.org/abs/2609.37187)