发表机构
Gran Sasso Science Institute (GSSI)（大萨索科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过文献研究结合实证验证，将机器人适应需求分为六类，分析经典及增强型行为树的能力局限，为机器人系统选择行为树方案提供指导。
AI 中文摘要
机器人系统日益在动态、不确定且开放的环境中运行，此时设计阶段的假设可能不再成立，为维持有效且安全的运行，适应变得必要。行为树（Behavior Trees, BTs）因模块化、可读性和反应性被广泛应用于机器人控制架构，这引发了一个核心问题：行为树是否足以满足现代机器人系统的适应需求？本文通过文献驱动的研究结合实证验证来探究该问题。首先，我们从文献中推导机器人适应需求的分类，将其组织为六个类别：知识、感知、致动、系统、任务和环境。接着，我们针对这些需求分析经典行为树的能力与局限性。然后，我们对现有文献中基于行为树的适应方法进行特征刻画，并将其分为四个主要类别：生成、扩展、演化和细化，包括结合多个类别的方法。我们的分析显示，经典行为树的模块化、灵活性和反应性不足以满足涉及运行时重构、不确定性下推理、任务重新解释、学习或与外部知识及规划机制集成的适应需求。增强型行为树方案解决了其中部分局限性，但程度各异且往往存在自身的局限性。我们的研究结果将适应需求与经典及增强型行为树的能力和局限性关联起来，为何时经典行为树足够、何时需要增强机制，以及自适应机器人控制架构仍存在或出现哪些挑战提供了指导。
英文摘要
Robotic systems increasingly operate in dynamic, uncertain, and open-ended environments, where design-time assumptions may no longer hold, and adaptation becomes necessary to maintain effective and safe operation. Behavior Trees (BTs) are widely used in robotic control architectures due to their modularity, readability, and reactivity. This raises a central question: are BTs sufficient to meet the adaptation needs of modern robotic systems? This paper investigates this question through a literature-driven study complemented by empirical validation. First, we derive a classification of robotic adaptation needs from the literature, organizing them into six categories: Knowledge, Perception, Actuation, System, Mission, and Environment. Then, we analyze the capabilities and limitations of classical BTs with respect to these needs. Then, we characterize BT-based approaches for adaptation from the existing literature and organize them into four primary families, i.e., generation, extension, evolution, and refinement, including approaches that combine multiple families. Our analysis shows that the modularity, flexibility, and reactivity of classical BTs are insufficient for adaptation needs involving runtime restructuring, reasoning under uncertainty, mission reinterpretation, learning, or integration with external knowledge and planning mechanisms. Enhanced BT approaches address several of these limitations, but to different extents and often with limitations of their own. Our findings relate adaptation needs to both the capabilities and limitations of classical and enhanced BTs, providing guidance on when classical BTs are sufficient, when enhanced mechanisms are needed, and which challenges remain or emerge for adaptive robotic control architectures.
