可重构行为树：面向满足高层决策和控制层特性的执行框架
Reconfigurable Behavior Trees: Towards an Executive Framework Meeting High-level Decision Making and Control Layer Features
摘要：
行为树构成了一种广泛的人工智能工具，已成功地在机器人技术中推广。它们的优点包括代码的简单性、模块性和可重用性。然而，行为树仍然是一个高层次的决策引擎;控制功能不能很容易地集成。本文提出了可重构行为树（RBT），传统的BT的扩展，考虑在决策过程中的机器人环境的物理约束。我们赋予RBT连续的感官信息，允许在线监测任务的执行。由此产生的刺激驱动的架构是能够动态地处理执行上下文的变化，同时保持低的执行时间。所提出的框架进行评估的一组机器人实验。结果表明，RBT是一种很有前途的机器人任务表示，监控和执行的方法。
英文摘要：
Behavior Trees constitute a widespread AI tool which has been successfully spun out in robotics. Their advantages include simplicity, modularity, and reusability of code. However, Behavior Trees remain a high-level decision making engine; control features cannot be easily integrated. This paper proposes the Reconfigurable Behavior Trees (RBTs), an extension of the traditional BTs that considers physical constraints from the robotic environment in the decision making process. We endow RBTs with continuous sensory information that permits the online monitoring of the task execution. The resulting stimulus-driven architecture is capable of dynamically handling changes in the executive context while keeping the execution time low. The proposed framework is evaluated on a set of robotic experiments. The results show that RBTs are a promising approach for robotic task representation, monitoring, and execution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
