让机器人在错误地图下仍能自主修正导航决策。
Context-Aware Replanning with Pre-explored Semantic Map for Object Navigation
- 通过置信度与多视角一致性评估地图不确定性
- 在对象导航任务中提升性能，无需额外标注
- 适用于依赖预构建语义地图的免训练机器人
通过视觉语言模型（VLMs）预先探索构建的语义地图，已成为免训练机器人应用的基础。然而，现有方法假设地图准确，缺乏基于错误地图修正决策的机制。为此，我们提出上下文感知重规划（CARe），通过置信度分数和多视角一致性估计地图不确定性，使智能体能在不依赖额外标签的情况下修正因地图不准导致的错误决策。我们将该方法集成到两种现代映射骨干网络（VLMaps 和 OpenMask3D）中，在物体导航任务中均取得显著性能提升。更多细节见项目页面：https://care-maps.github.io/
原文摘要 · Abstract (English)
Pre-explored Semantic Maps, constructed through prior exploration using visual language models (VLMs), have proven effective as foundational elements for training-free robotic applications. However, existing approaches assume the map's accuracy and do not provide effective mechanisms for revising decisions based on incorrect maps. To address this, we introduce Context-Aware Replanning (CARe), which estimates map uncertainty through confidence scores and multi-view consistency, enabling the agent to revise erroneous decisions stemming from inaccurate maps without requiring additional labels. We demonstrate the effectiveness of our proposed method by integrating it with two modern mapping backbones, VLMaps and OpenMask3D, and observe significant performance improvements in object navigation tasks. More details can be found on the project page: https://care-maps.github.io/
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