自动构建完整机器人行为树系统，减少人工设计依赖。
CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation
- 利用大模型启发式搜索动作模型与控制策略
- 在7个任务集上实现完整一致的行为树生成
- 适合需快速部署机器人的场景
行为树（BT）为设计模块化、响应式机器人控制器提供了强大范式。BT规划作为新兴领域，能理论上保证可靠行为树的自动生成。然而，传统方法通常假设已有完善的已接地行为树系统——包含高层动作模型和底层控制策略——这往往需要大量专家知识和手动工作。本文形式化了行为树接地问题：自动化构建完整且一致的行为树系统。我们分析其复杂性，并提出CABTO（上下文感知行为树接地）框架，首次高效解决该挑战。CABTO利用预训练大模型（LMs）启发式搜索动作模型与控制策略空间，由行为树规划器和环境观测提供的上下文反馈引导。在三个不同机器人操作场景下的七个任务集实验表明，CABTO能有效高效地生成完整一致的行为树系统。
原文摘要 · Abstract (English)
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.
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