ニューロシンボリック言語モデルと多段階ゴール分解による高速かつ高精度なタスクプランニング
Fast and Accurate Task Planning using Neuro-Symbolic Language Models and Multi-level Goal Decomposition
LLMで複雑なタスクをサブゴールに分解し、各サブゴールの複雑さに応じて記号プランナかMCTSベースのLLMプランナを使い分けることで、計画時間を短縮しつつ成功率を高めるニューロシンボリック手法を提案した。
著者: Minseo Kwon, Yaesol Kim, Young J. Kim
分類: cs.RO
原文アブストラクト
In robotic task planning, symbolic planners using rule-based representations like PDDL are effective but struggle with long-sequential tasks in complicated environments due to exponentially increasing search space. Meanwhile, LLM-based approaches, which are grounded in artificial neural networks, offer faster inference and commonsense reasoning but suffer from lower success rates. To address the limitations of the current symbolic (slow speed) or LLM-based approaches (low accuracy), we propose a novel neuro-symbolic task planner that decomposes complex tasks into subgoals using LLM and carries out task planning for each subgoal using either symbolic or MCTS-based LLM planners, depending on the subgoal complexity. This decomposition reduces planning time and improves success rates by narrowing the search space and enabling LLMs to focus on more manageable tasks. Our method significantly reduces planning time while maintaining high success rates across task planning domains, as well as real-world and simulated robotics environments. More details are available at http://graphics.ewha.ac.kr/LLMTAMP/.
