タスク推論のための関係性を考慮した階層的3Dシーングラフ
Relationship-Aware Hierarchical 3D Scene Graph for Task Reasoning
VLMとLLMを組み合わせ、オープンボキャブラリ特徴と物体間関係を統合した階層的3Dシーングラフを構築し、四足ロボットでタスク推論を実証した研究。
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著者: Albert Gassol Puigjaner, Angelos Zacharia, Kostas Alexis
分類: cs.RO
原文アブストラクト
Representing and understanding 3D environments in a structured manner is crucial for autonomous agents to navigate and reason about their surroundings. While traditional Simultaneous Localization and Mapping (SLAM) methods generate metric reconstructions and can be extended to metric-semantic mapping, they lack a higher level of abstraction and relational reasoning. To address this gap, 3D scene graphs have emerged as a powerful representation for capturing hierarchical structures and object relationships. In this work, we propose an enhanced hierarchical 3D scene graph that integrates open-vocabulary features across multiple abstraction levels and supports object-relational reasoning. Our approach leverages a Vision Language Model (VLM) to infer semantic relationships. Notably, we introduce a task reasoning module that combines Large Language Models (LLM) and a VLM to interpret the scene graph's semantic and relational information, enabling agents to reason about tasks and interact with their environment more intelligently. We validate our method by deploying it on a quadruped robot in multiple environments and tasks, highlighting its ability to reason about them.
