SG-CoT: シーングラフ表現を用いた曖昧性対応ロボット計画フレームワーク
SG-CoT: An Ambiguity-Aware Robotic Planning Framework using Scene Graph Representations
環境をシーングラフで表現し、LLMがそれを反復的に照会して曖昧性を検出・解消する2段階のロボット計画手法を提案。単一・複数エージェント環境で従来手法より高い成功率と質問精度を達成。
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著者: Akshat Rana, Peeyush Agarwal, K. P. S. Rana, Amarjit Malhotra
分類: cs.RO
原文アブストラクト
Ambiguity poses a major challenge to large language models (LLMs) used as robotic planners. In this letter, we present Scene Graph-Chain-of-Thought (SG-CoT), a two-stage framework where LLMs iteratively query a scene graph representation of the environment to detect and clarify ambiguities. First, a structured scene graph representation of the environment is constructed from input observations, capturing objects, their attributes, and relationships with other objects. Second, the LLM is equipped with retrieval functions to query portions of the scene graph that are relevant to the provided instruction. This grounds the reasoning process of the LLM in the observation, increasing the reliability of robotic planners under ambiguous situations. SG-CoT also allows the LLM to identify the source of ambiguity and pose a relevant disambiguation question to the user or another robot. Extensive experimentation demonstrates that SG-CoT consistently outperforms prior methods, with a minimum of 10% improvement in question accuracy and a minimum success rate increase of 4% in single-agent and 15% in multi-agent environments, validating its effectiveness for more generalizable robot planning.
