Natural language provides a natural interface for human communication, yet it is challenging for robots to comprehend due to its abstract nature and inherent ambiguity. Large language models (LLMs) contain commonsense knowledge that can help resolve language ambiguity and generate possible solutions to abstract specifications. While LLMs have shown promise as few-shot planning policies, their potential for planning complex tasks is not fully tapped. This paper shows that LLMs can be used as both the commonsense model of the world and the heuristic policy in search algorithms such as Monte Carlo Tree Search (MCTS). MCTS explores likely world states sampled from LLMs to facilitate better-reasoned decision-making. The commonsense policy from LLMs guides the search to relevant parts of the tree, substantially reducing the search complexity. We demonstrate the effectiveness of our method in daily task-planning experiments and highlight its advantages over using LLMs solely as policies.

翻译：自然语言为人类交流提供了自然接口，但由于其抽象性和固有的歧义性，机器人难以理解。大语言模型（LLMs）包含常识知识，有助于解决语言歧义并为抽象规范生成可能的解决方案。尽管LLMs在少样本规划策略方面展现潜力，但其在复杂任务规划中的能力尚未被充分挖掘。本文表明，LLMs既可作为世界的常识模型，又可作为蒙特卡洛树搜索（MCTS）等搜索算法中的启发式策略。MCTS探索从LLMs采样的可能世界状态，以促进更合理的决策制定。LLMs提供的常识策略引导搜索至树的相关分支，从而大幅降低搜索复杂度。我们通过日常任务规划实验证明了方法的有效性，并突出了其相较于仅将LLMs作为策略使用的优势。