Behavior Tree (BT) planning is crucial for autonomous robot behavior control, yet its application in complex scenarios is hampered by long planning times. Pruning and heuristics are common techniques to accelerate planning, but it is difficult to design general pruning strategies and heuristic functions for BT planning problems. This paper proposes improving BT planning efficiency for everyday service robots leveraging commonsense reasoning provided by Large Language Models (LLMs), leading to model-free pre-planning action space pruning and heuristic generation. This approach takes advantage of the modularity and interpretability of BT nodes, represented by predicate logic, to enable LLMs to predict the task-relevant action predicates and objects, and even the optimal path, without an explicit action model. We propose the Heuristic Optimal Behavior Tree Expansion Algorithm (HOBTEA) with two heuristic variants and provide a formal comparison and discussion of their efficiency and optimality. We introduce a learnable and transferable commonsense library to enhance the LLM's reasoning performance without fine-tuning. The action space expansion based on the commonsense library can further increase the success rate of planning. Experiments show the theoretical bounds of commonsense pruning and heuristic, and demonstrate the actual performance of LLM learning and reasoning with the commonsense library. Results in four datasets showcase the practical effectiveness of our approach in everyday service robot applications.

翻译：行为树（BT）规划对于自主机器人行为控制至关重要，但其在复杂场景中的应用因规划时间过长而受到限制。剪枝与启发式是加速规划的常用技术，但为BT规划问题设计通用的剪枝策略与启发式函数较为困难。本文提出利用大型语言模型（LLMs）提供的常识推理来提升日常服务机器人的BT规划效率，从而实现无需显式动作模型的预规划动作空间剪枝与启发式生成。该方法利用以谓词逻辑表示的BT节点的模块化与可解释性优势，使LLMs能够预测任务相关的动作谓词与对象，甚至最优路径，而无需依赖显式动作模型。我们提出了启发式最优行为树扩展算法（HOBTEA），包含两种启发式变体，并对其效率与最优性进行了形式化比较与讨论。我们引入了一个可学习、可迁移的常识库，以在不进行微调的情况下提升LLM的推理性能。基于该常识库的动作空间扩展能进一步提高规划的成功率。实验展示了常识剪枝与启发式的理论边界，并验证了LLM借助常识库进行学习与推理的实际性能。在四个数据集上的结果证明了该方法在日常服务机器人应用中的实际有效性。