Tree-Planner: Efficient Close-loop Task Planning with Large Language
  Models
Mengkang Hu Yao Mu Xinmiao Yu Mingyu Ding Shiguang Wu Wenqi Shao Qiguang Chen Bin Wang Yu Qiao Ping Luo
This paper studies close-loop task planning, which refers to the process of
generating a sequence of skills (a plan) to accomplish a specific goal while
adapting the plan based on real-time observations. Recently, prompting Large
Language Models (LLMs) to generate actions iteratively has become a prevalent
paradigm due to its superior performance and user-friendliness. However, this
paradigm is plagued by two inefficiencies: high token consumption and redundant
error correction, both of which hinder its scalability for large-scale testing
and applications. To address these issues, we propose Tree-Planner, which
reframes task planning with LLMs into three distinct phases: plan sampling,
action tree construction, and grounded deciding. Tree-Planner starts by using
an LLM to sample a set of potential plans before execution, followed by the
aggregation of them to form an action tree. Finally, the LLM performs a
top-down decision-making process on the tree, taking into account real-time
environmental information. Experiments show that Tree-Planner achieves
state-of-the-art performance while maintaining high efficiency. By decomposing
LLM queries into a single plan-sampling call and multiple grounded-deciding
calls, a considerable part of the prompt are less likely to be repeatedly
consumed. As a result, token consumption is reduced by 92.2% compared to the
previously best-performing model. Additionally, by enabling backtracking on the
action tree as needed, the correction process becomes more flexible, leading to
a 40.5% decrease in error corrections. Project page:
https://tree-planner.github.io/