Large Language Models as Commonsense Knowledge for Large-Scale Task Planning

Zirui Zhao, Wee Sun Lee, David Hsu

Keywords: Embodied Task Planning, Large Language Models, Human-Robot Interaction

TL;DR: We use Large Language Models as both the commonsense world model and the heuristic policy within the Monte Carlo Tree Search framework, enabling better-reasoned decision-making for daily tasks.

Abstract: 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 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.