大規模言語モデルによる部分観測タスク・動作計画の状態推定ガイド
Large-Language-Model-Guided State Estimation for Partially Observable Task and Motion Planning
ロボットが部分観測環境で計画を実行中に予期しない物体を観測した際、LLMの常識知識を用いてタスク関連物体の信念を形成し、計画・実行時間を大幅に短縮するフレームワークCoCo-TAMPを提案した。
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著者: Yoonwoo Kim, Raghav Arora, Roberto Martín-Martín, Peter Stone, Ben Abbatematteo, Yoonchang Sung
分類: cs.RO, cs.AI
原文アブストラクト
Robot planning in partially observable environments, where not all objects are known or visible, is a challenging problem, as it requires reasoning under uncertainty through partially observable Markov decision processes. During the execution of a computed plan, a robot may unexpectedly observe task-irrelevant objects, which are typically ignored by naive planners. In this work, we propose incorporating two types of common-sense knowledge: (1) certain objects are more likely to be found in specific locations; and (2) similar objects are likely to be co-located, while dissimilar objects are less likely to be found together. Manually engineering such knowledge is complex, so we explore leveraging the powerful common-sense reasoning capabilities of large language models (LLMs). Our planning and execution framework, CoCo-TAMP, introduces a hierarchical state estimation that uses LLM-guided information to shape the belief over task-relevant objects, enabling efficient solutions to long-horizon task and motion planning problems. In experiments, CoCo-TAMP achieves an average reduction of 62.7% in planning and execution time in simulation, and 72.6% in real-world demonstrations, compared to a baseline that does not incorporate either type of common-sense knowledge.
