Behavior Trees (BTs) were first conceived in the computer games industry as a tool to model agent behavior, but they received interest also in the robotics community as an alternative policy design to Finite State Machines (FSMs). The advantages of BTs over FSMs had been highlighted in many works, but there is no thorough practical comparison of the two designs. Such a comparison is particularly relevant in the robotic industry, where FSMs have been the state-of-the-art policy representation for robot control for many years. In this work we shed light on this matter by comparing how BTs and FSMs behave when controlling a robot in a mobile manipulation task. The comparison is made in terms of reactivity, modularity, readability, and design. We propose metrics for each of these properties, being aware that while some are tangible and objective, others are more subjective and implementation dependent. The practical comparison is performed in a simulation environment with validation on a real robot. We find that although the robot's behavior during task solving is independent on the policy representation, maintaining a BT rather than an FSM becomes easier as the task increases in complexity.

翻译：行为树最初由计算机游戏行业提出，用于建模智能体行为，随后在机器人学界作为有限状态机的替代策略设计方法受到关注。尽管众多研究已强调行为树相较于有限状态机的优势，但二者之间尚缺乏系统性的实践对比。此类对比在机器人工业领域尤为重要，因为有限状态机多年来一直是机器人控制策略表征的主流方法。本研究通过对比行为树与有限状态机在移动操作任务中控制机器人的表现，对此问题展开深入探讨。我们从反应性、模块化、可读性及设计维度进行比较，并为每个属性提出量化指标——我们注意到部分属性具有客观可测性，而另一些则更具主观性且依赖于具体实现。实践对比在仿真环境中进行，并在真实机器人平台上完成验证。研究发现：虽然机器人在任务执行过程中的行为表现与策略表征方式无关，但随着任务复杂度的提升，维护行为树相比维护有限状态机更具优势。