Hi-Dyna Graph：面向以人为中心环境中机器人自主性的层次化动态场景图
Hi-Dyna Graph: Hierarchical Dynamic Scene Graph for Robotic Autonomy in Human-Centric Environments
AI总结：
本文提出 Hi-Dyna Graph 层次化动态场景图架构，融合全局拓扑与局部动态语义，并结合大语言模型智能体实现以人为中心动态环境中的机器人自主任务执行。
摘要：
服务机器人在以人为中心的场景中自主运行仍然具有挑战性，因为需要理解不断变化的环境并进行上下文感知的决策。虽然拓扑地图等现有方法提供了高效的空间先验，但它们无法建模瞬态物体关系，而密集神经表示（如 NeRF）则带来难以承受的计算成本。受层次化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，一种层次化动态场景图架构，将持久性全局布局与局部动态语义相结合，用于具身机器人自主性。我们的框架从带位姿的 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物体（如家具），同时环境相机和以自我为中心的相机构建动态子图，包含物体位置关系和人与物体交互模式。通过使用语义和空间约束将这些子图锚定到全局拓扑上，构建了一种混合架构，使环境演化时能够无缝更新。一个由大语言模型（LLMs）驱动的智能体被用于解释统一图，推断潜在任务触发条件，并生成基于机器人可供性的可执行指令。我们进行了复杂实验，以证明 Hi-Dyna Graph 优越的场景表示有效性。真实世界部署通过移动机械臂验证了系统的实用性：机器人在动态场景中作为自助餐厅助手，无需进一步训练或复杂奖励即可自主完成复杂任务。视频演示和更多细节见 https://anonymous.4open.science/r/Hi-Dyna-Graph-B326。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
