标题： Hi-Dyna 图：以人为中心环境中机器人自主的分层动态场景图 显示英文标题

标题： Hi-Dyna Graph: Hierarchical Dynamic Scene Graph for Robotic Autonomy in Human-Centric Environments

摘要： 在需要理解变化的环境和情境感知决策的人本场景中，服务机器人自主运行仍具有挑战性。 尽管现有方法（如拓扑图）提供了高效的空间先验，但它们无法建模短暂的对象关系，而密集神经表示（例如NeRF）则带来了高昂的计算成本。 受到分层场景表示和视频场景图生成工作的启发，我们提出了Hi-Dyna Graph，这是一种分层动态场景图架构，集成了持久的全局布局与局部化的动态语义，以实现具身机器人的自主性。 我们的框架从姿态RGB-D输入构建全局拓扑图，编码房间尺度的连通性和大型静态对象（例如家具），同时环境和自我中心摄像机使用物体位置关系和人机交互模式填充动态子图。 通过使用语义和空间约束将这些子图锚定到全局拓扑中，进行混合架构，从而随着环境的变化实现无缝更新。 我们采用由大型语言模型（LLMs）驱动的代理来解释统一图，推断潜在的任务触发器，并生成基于机器人能力的可执行指令。 我们进行了复杂的实验，证明了Hi-Dyna Graph在场景表示方面的优越效果。 现实世界的部署验证了该系统在移动操作机器人上的实用性：机器人无需进一步训练或复杂奖励即可在动态场景（如自助餐厅助手）中自主完成复杂任务。 请访问https://anonymous.4open.science/r/Hi-Dyna-Graph-B326观看视频演示和更多详细信息。 显示英文摘要

摘要： Autonomous operation of service robotics in human-centric scenes remains challenging due to the need for understanding of changing environments and context-aware decision-making. While existing approaches like topological maps offer efficient spatial priors, they fail to model transient object relationships, whereas dense neural representations (e.g., NeRF) incur prohibitive computational costs. Inspired by the hierarchical scene representation and video scene graph generation works, we propose Hi-Dyna Graph, a hierarchical dynamic scene graph architecture that integrates persistent global layouts with localized dynamic semantics for embodied robotic autonomy. Our framework constructs a global topological graph from posed RGB-D inputs, encoding room-scale connectivity and large static objects (e.g., furniture), while environmental and egocentric cameras populate dynamic subgraphs with object position relations and human-object interaction patterns. A hybrid architecture is conducted by anchoring these subgraphs to the global topology using semantic and spatial constraints, enabling seamless updates as the environment evolves. An agent powered by large language models (LLMs) is employed to interpret the unified graph, infer latent task triggers, and generate executable instructions grounded in robotic affordances. We conduct complex experiments to demonstrate Hi-Dyna Grap's superior scene representation effectiveness. Real-world deployments validate the system's practicality with a mobile manipulator: robotics autonomously complete complex tasks with no further training or complex rewarding in a dynamic scene as cafeteria assistant. See https://anonymous.4open.science/r/Hi-Dyna-Graph-B326 for video demonstration and more details.