LLMによる前提条件とサブゴール生成で認知ロボティクスを常識で強化する
Enhancing Cognitive Robotics with Commonsense through LLM-Generated Preconditions and Subgoals
大規模言語モデルと記号プランニングを組み合わせ、自然言語タスクから常識的な前提条件やサブゴールを生成してロボットの計画立案を改善する手法を提案し、シミュレーションで有効性を示した。
著者: Ohad Bachner, Bar Gamliel
分類: cs.RO, cs.AI
原文アブストラクト
Robots often fail at everyday tasks because instructions skip commonsense details like hidden preconditions and small subgoals. Traditional symbolic planners need these details to be written explicitly, which is time consuming and often incomplete. In this project we combine a Large Language Model with symbolic planning. Given a natural language task, the LLM suggests plausible preconditions and subgoals. We translate these suggestions into a formal planning model and execute the resulting plan in simulation. Compared to a baseline planner without the LLM step, our system produces more valid plans, achieves a higher task success rate, and adapts better when the environment changes. These results suggest that adding LLM commonsense to classical planning can make robot behavior in realistic scenarios more reliable.