将空间物体与动态事件关联，让机器人理解环境中的复杂时空关系。
Event-Grounding Graph: Unified Spatio-Temporal Scene Graph from Robotic Observations
- 构建事件锚定图，连接物体位置与发生事件
- 在真实机器人数据上实现精准的时空问答响应
- 适合需要环境理解的智能机器人研发者使用
构建能协助人类日常生活的智能自主机器人，关键在于建立丰富的环境表征。尽管语义场景表示的进步提升了机器人的场景理解能力，但现有方法缺乏空间特征与动态事件之间的关联；例如，无法将蓝色杯子与‘洗杯子’这一事件关联起来。本文提出事件锚定图（EGG）框架，将事件交互与场景的空间特征进行锚定。该表示使机器人能够感知、推理并响应复杂的时空查询。基于真实机器人数据的实验表明，EGG具备准确检索相关信息并回应人类关于环境与事件问题的能力。此外，EGG框架的源代码和评估数据集已开源：https://github.com/aalto-intelligent-robotics/EGG。
原文摘要 · Abstract (English)
A fundamental aspect for building intelligent autonomous robots that can assist humans in their daily lives is the construction of rich environmental representations. While advances in semantic scene representations have enriched robotic scene understanding, current approaches lack a connection between spatial features and dynamic events; e.g., connecting the blue mug to the event washing a mug. In this work, we introduce the event-grounding graph (EGG), a framework grounding event interactions to spatial features of a scene. This representation allows robots to perceive, reason, and respond to complex spatio-temporal queries. Experiments using real robotic data demonstrate EGG's capability to retrieve relevant information and respond accurately to human inquiries concerning the environment and events within. Furthermore, the EGG framework's source code and evaluation dataset are released as open-source at: https://github.com/aalto-intelligent-robotics/EGG.
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