Integrating Intent Understanding and Optimal Behavior Planning for
Behavior Tree Generation from Human instructions

Xinglin Chen*,   Yishuai Cai*,  Yunxin Mao,  Minglong Li↑,  
Wenjing Yang,  Weixia Xu, Ji Wang

College of Computer Science and Technology,
National University of Defense Technology


Robots executing tasks following human instructions in domestic or industrial environments essentially require both adaptability and reliability. Behavior Trees (BTs) emerge as an appropriate control architecture for these scenarios due to their 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 hospitality industry settings 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.

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