Behavior Trees (BTs) offer a powerful paradigm for designing modular and reactive robot controllers. BT planning, an emerging field, provides theoretical guarantees for the automated generation of reliable BTs. However, BT planning typically assumes that a well-designed BT system is already grounded -- comprising high-level action models and low-level control policies -- which often requires extensive expert knowledge and manual effort. In this paper, we formalize the BT Grounding problem: the automated construction of a complete and consistent BT system. We analyze its complexity and introduce CABTO (Context-Aware Behavior Tree grOunding), the first framework to efficiently solve this challenge. CABTO leverages pre-trained Large Models (LMs) to heuristically search the space of action models and control policies, guided by contextual feedback from BT planners and environmental observations. Experiments spanning seven task sets across three distinct robotic manipulation scenarios demonstrate CABTO's effectiveness and efficiency in generating complete and consistent behavior tree systems.

翻译：行为树（BTs）为设计模块化、反应式的机器人控制器提供了强大的范式。行为树规划作为一个新兴领域，为自动生成可靠行为树提供了理论保证。然而，行为树规划通常假设一个设计良好的行为树系统已经完成接地——即包含高层动作模型与底层控制策略——这往往需要大量专家知识与人工投入。本文形式化定义了行为树接地问题：即自动化构建完整且一致的行为树系统。我们分析了该问题的复杂度，并提出了CABTO（上下文感知行为树接地）——首个能高效解决此挑战的框架。CABTO利用预训练大模型（LMs）在行为树规划器的上下文反馈与环境观测的引导下，启发式搜索动作模型与控制策略的空间。在三个不同机器人操作场景下跨越七个任务集的实验表明，CABTO在生成完整且一致的行为树系统方面具有显著的有效性与高效性。