Graph-MambaNav：利用对象关系知识的时空图Mamba用于目标对象导航
Graph-MambaNav: Spatial-Temporal Graph Mamba Leveraging Object-Relation Knowledge for Object-Goal Navigation
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中文总结 AI 辅助
本研究提出Graph-MambaNav，一种结合LLM常识对象关系的目标感知时空图编码框架，通过节点排序与Mamba建模实现高效导航，在仿真环境表现优异且泛化性好，经真实机器人部署验证有效。
中文摘要 AI 辅助
目标对象导航要求智能体在未知环境中推理对象间关系，并优先处理与目标相关的对象以实现高效决策。现有基于图的方法虽在特征或注意力层面融入目标感知，但仍保持置换不变性，缺乏明确机制控制信息传播顺序，限制了其对目标依赖重要性及长程依赖的建模能力。相比之下，Graph-Mamba强调通过序列排序实现节点优先级对有效全局推理至关重要。本研究探讨Graph-Mamba中的节点优先级机制及其在对象导航中的作用，提出Graph-MambaNav，这是一种目标感知的时空图编码框架，它基于对象与目标的相关性引入对象的启发式排序，使信息更丰富的对象能被后续处理以聚合更丰富的上下文。节点排序和边权重均从大语言模型（LLM）衍生的常识对象关系初始化，为结构化推理提供统一先验。空间模块将局部消息传递与基于全局GraphMamba的选择性扫描相结合，时间模块则基于对象级时间顺序应用Mamba序列建模，允许选择性聚合历史上下文以进行长程时间推理。在AI2-THOR和RoboTHOR上的实验表明，该方法提升了导航性能且具备泛化能力，额外的真实世界机器人部署进一步验证了所提方法的有效性。
英文摘要
Object-goal navigation requires an agent to reason over object relationships and prioritize target-relevant objects for efficient decision making in unseen environments. While existing graph-based methods incorporate target-awareness at the feature or attention level, they remain permutation-invariant and lack an explicit mechanism to control information propagation order, limiting their ability to model target-dependent importance and long-range dependencies. In contrast, Graph-Mamba highlights that node prioritization through sequence ordering is critical for effective global reasoning. In this work, we investigate the node prioritization mechanism in Graph-Mamba and study its role in object navigation. We propose Graph-MambaNav, a target-aware spatial-temporal graph encoding framework that introduces a heuristic ordering over objects based on their relevance to the target, allowing more informative objects to be processed later to aggregate richer context. Both node ordering and edge weights are initialized from LLM-derived commonsense object relationships, providing a unified prior for structured reasoning. A spatial module integrates local message passing with global GraphMamba-based selective scanning, while a temporal module applies Mamba-based sequence modeling over object-wise temporal orders, allowing selective aggregation of historical context for long-range temporal reasoning. Experiments on AI2-THOR and RoboTHOR demonstrate improved navigation performance with generalization, and additional real-world robot deployment further validates the effectiveness of our proposed approach.
发表机构
- School of Internet of Things Engineering, Wuxi University（无锡大学物联网工程学院）
- School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications（南京邮电大学通信与信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。
