概念引导探索：构建持久、可执行的场景图
Concept-Guided Exploration: Building Persistent, Actionable Scene Graphs
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中文总结 AI 辅助
该研究提出以概念为先的认知分布式架构，通过房间、门两类概念智能体协作，基于分层约束传播与预测-匹配循环构建无需全局度量地图的持久可执行室内场景图，实现机器人空间理解。
中文摘要 AI 辅助
移动机器人对3D空间的感知正迅速从平面度量网格表示转向由人类可解释概念构建的混合度量-语义图。多数方法先构建度量地图再添加语义层，本文探索了一种替代的、以概念为先的架构，其中空间理解源于异步概念智能体，这些智能体直接实例化并管理语义实体。机器人采用两个空间概念（房间和门），在认知分布式架构中作为自主进程实现。这些概念智能体通过主动探索和增量验证，协作构建室内布局的共享场景图表示。关键架构原则是分层约束传播：房间实例化为墙壁边界内的门检测提供几何和语义先验以引导和支持。所得结构由基于预测-匹配循环的互补功能原则维护。该方法旨在生成可执行、人类可解释的空间表示，无需依赖任何预先存在的全局度量地图，支持结构化室内环境中的可扩展操作以及持久、与任务相关的理解。
英文摘要
The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-interpretable concepts. While most approaches first build metric maps and then add semantic layers, we explore an alternative, concept-first architecture in which spatial understanding emerges from asynchronous concept agents that directly instantiate and manage semantic entities. Our robot employs two spatial concepts (room and door), implemented as autonomous processes within a cognitive distributed architecture. These concept agents cooperatively build a shared scene graph representation of indoor layouts through active exploration and incremental validation. The key architectural principle is hierarchical constraint propagation: Room instantiation provides geometric and semantic priors to guide and support door detection within wall boundaries. The resulting structure is maintained by a complementary functional principle based on prediction-matching loops. This approach is designed to yield an actionable, human-interpretable spatial representation without relying on any pre-existing global metric map, supporting scalable operation and persistent, task-relevant understanding in structured indoor environments.
发表机构
- University of Extremadura（埃斯特雷马杜拉大学）
机构由 AI 辅助整理，请以论文原文为准。
