Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions
arxiv(2024)
摘要
Robots executing tasks following human instructions in domestic or industrial
environments essentially require both adaptability and reliability. Behavior
Tree (BT) emerges as an appropriate control architecture for these scenarios
due to its modularity and reactivity. Existing BT generation methods, however,
either do not involve interpreting natural language or cannot theoretically
guarantee the BTs' success. This paper proposes a two-stage framework for BT
generation, which first employs large language models (LLMs) to interpret goals
from high-level instructions, then constructs an efficient goal-specific BT
through the Optimal Behavior Tree Expansion Algorithm (OBTEA). We represent
goals as well-formed formulas in first-order logic, effectively bridging intent
understanding and optimal behavior planning. Experiments in the service robot
validate the proficiency of LLMs in producing grammatically correct and
accurately interpreted goals, demonstrate OBTEA's superiority over the baseline
BT Expansion algorithm in various metrics, and finally confirm the practical
deployability of our framework. The project website is
https://dids-ei.github.io/Project/LLM-OBTEA/.
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