论文标题
行为树执行的交互式歧义
Interactive Disambiguation for Behavior Tree Execution
论文作者
论文摘要
近年来，机器人用于越来越多的任务，尤其是中小型企业。这些任务通常是快速变化的，它们具有协作的场景，并且发生在具有歧义的不可预测环境中。重要的是要有能够轻松生成机器人程序的方法，这些方法是通过处理不确定性尽可能笼统的。我们提出了一个系统，该系统集成了一种学习行为树（BTS）的方法，从示范和放置任务的示范中，一个使用语言互动来提出后续澄清问题以解决歧义。在执行任务期间，当需要在场景中歧义对象时，该任务的目标是多个实例中存在的同一类型的对象时，系统会要求用户输入。在挑选和放置任务的不同情况下，综合系统的歧义水平提高了。本文使用的代码公开可用。
In recent years, robots are used in an increasing variety of tasks, especially by small- and medium- sized enterprises. These tasks are usually fast-changing, they have a collaborative scenario and happen in unpredictable environments with possible ambiguities. It is important to have methods capable of generating robot programs easily, that are made as general as possible by handling uncertainties. We present a system that integrates a method to learn Behavior Trees (BTs) from demonstration for pick and place tasks, with a framework that uses verbal interaction to ask follow-up clarification questions to resolve ambiguities. During the execution of a task, the system asks for user input when there is need to disambiguate an object in the scene, when the targets of the task are objects of a same type that are present in multiple instances. The integrated system is demonstrated on different scenarios of a pick and place task, with increasing level of ambiguities. The code used for this paper is made publicly available.
