Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning

Yishuai Cai, Xinglin Chen, Yunxin Mao, Minglong Li, Shaowu Yang, Wenjing Yang, Ji Wang

TL;DR

HBTP addresses scalability and reliability gaps in Behavior Tree planning by integrating task-specific reasoning from Large Language Models to generate a heuristic path that guides BT expansion. It introduces two heuristic variants (optimal and satisficing), action-space pruning, and reflective feedback to mitigate LLM inaccuracies. Theoretical analysis and experiments across four service-robot datasets show substantial planning-time reductions while preserving planning quality, with reflective feedback further enhancing LLM reasoning and robustness. The approach enables fast, reactive, and reliable BT generation, making BT planning more practical for real-world robotic applications.

Abstract

Behavior Trees (BTs) are increasingly becoming a popular control structure in robotics due to their modularity, reactivity, and robustness. In terms of BT generation methods, BT planning shows promise for generating reliable BTs. However, the scalability of BT planning is often constrained by prolonged planning times in complex scenarios, largely due to a lack of domain knowledge. In contrast, pre-trained Large Language Models (LLMs) have demonstrated task reasoning capabilities across various domains, though the correctness and safety of their planning remain uncertain. This paper proposes integrating BT planning with LLM reasoning, introducing Heuristic Behavior Tree Planning (HBTP)-a reliable and efficient framework for BT generation. The key idea in HBTP is to leverage LLMs for task-specific reasoning to generate a heuristic path, which BT planning can then follow to expand efficiently. We first introduce the heuristic BT expansion process, along with two heuristic variants designed for optimal planning and satisficing planning, respectively. Then, we propose methods to address the inaccuracies of LLM reasoning, including action space pruning and reflective feedback, to further enhance both reasoning accuracy and planning efficiency. Experiments demonstrate the theoretical bounds of HBTP, and results from four datasets confirm its practical effectiveness in everyday service robot applications.

HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning

TL;DR

HBTP addresses scalability and reliability gaps in Behavior Tree planning by integrating task-specific reasoning from Large Language Models to generate a heuristic path that guides BT expansion. It introduces two heuristic variants (optimal and satisficing), action-space pruning, and reflective feedback to mitigate LLM inaccuracies. Theoretical analysis and experiments across four service-robot datasets show substantial planning-time reductions while preserving planning quality, with reflective feedback further enhancing LLM reasoning and robustness. The approach enables fast, reactive, and reliable BT generation, making BT planning more practical for real-world robotic applications.

Abstract

Behavior Trees (BTs) are increasingly becoming a popular control structure in robotics due to their modularity, reactivity, and robustness. In terms of BT generation methods, BT planning shows promise for generating reliable BTs. However, the scalability of BT planning is often constrained by prolonged planning times in complex scenarios, largely due to a lack of domain knowledge. In contrast, pre-trained Large Language Models (LLMs) have demonstrated task reasoning capabilities across various domains, though the correctness and safety of their planning remain uncertain. This paper proposes integrating BT planning with LLM reasoning, introducing Heuristic Behavior Tree Planning (HBTP)-a reliable and efficient framework for BT generation. The key idea in HBTP is to leverage LLMs for task-specific reasoning to generate a heuristic path, which BT planning can then follow to expand efficiently. We first introduce the heuristic BT expansion process, along with two heuristic variants designed for optimal planning and satisficing planning, respectively. Then, we propose methods to address the inaccuracies of LLM reasoning, including action space pruning and reflective feedback, to further enhance both reasoning accuracy and planning efficiency. Experiments demonstrate the theoretical bounds of HBTP, and results from four datasets confirm its practical effectiveness in everyday service robot applications.
Paper Structure (36 sections, 4 theorems, 6 equations, 7 figures, 4 tables, 1 algorithm)

This paper contains 36 sections, 4 theorems, 6 equations, 7 figures, 4 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. Preliminaries
  3. Methodology
  4. Heuristics Design and Formal Analysis
  5. Paths in BT Planning
  6. Heuristic Action Indicator
  7. Optimal Heuristics
  8. Satisficing Heuristics
  9. Heuristic Behavior Tree Planning
  10. Task-Specific Reasoning with Large Language Models
  11. Prompt Engineering
  12. Task-Specific Reasoning
  13. Action Space Purning
  14. Reflective Feedback
  15. Experiments
  16. ...and 21 more sections

Key Result

Proposition 1

Given a task $<s_0,g>$ and a heuristic path $\hat{p}$, if $I(\hat{p},a)\leq I(p^*,a), \forall a\in\mathcal{A}$ and $\alpha \geq 1$, we have $\forall p \in \mathcal{P}(s_0,g), h^\alpha (p^*,\hat{p}) \leq h^\alpha (p,\hat{p})$.

Figures (7)

  • Figure 1: The key concept of integrating BT planning with LLM reasoning. Given the task information, the LLM can divide the action space into heuristic actions, relevant actions, and irrelevant actions, effectively guiding heuristic BT planning.
  • Figure 2: An overview of our framework. (1) Before HBTP, a heuristic path reasoned by the LLM is used to construct a heuristic action indicator. During HBTP, condition nodes in the BT are ranked and expanded based on their heuristic values through exploration until the initial state is reached, with the BT expanding accordingly. (2) For LLM reasoning, the scene and task are input to the LLM, which outputs task-relevant predicates and objects to prune the action space. If planning fails or times out, BT summaries are utilized to further refine the heuristics. (3) After HBTP, the produced BT is implemented into the robot to perform the task reactively and robustly.
  • Figure 3: Comparison of planning time.
  • Figure 4: Impact of correct and error rate for two HBTP algorithms with different heuristics. ER stands for error rates.
  • Figure 5: Illustrations of scenarios
  • ...and 2 more figures

Theorems & Definitions (7)

  • Proposition 1
  • proof
  • Proposition 2
  • Proposition 3
  • proof
  • Proposition 1
  • proof