Teaching task-level directives to robots via demonstration is a popular tool to expand the robot's capabilities to interact with its environment. While current learning from demonstration systems primarily focuses on abstracting the task-level knowledge to the robot, these systems lack the ability to understand which part of the task can be already solved given the robot's prior knowledge. Therefore, instead of only requiring demonstrations of the missing pieces, these systems will require a demonstration of the complete task, which is cumbersome, repetitive, and can discourage people from helping the robot by performing the demonstrations. Therefore, we propose to use the notion of "excuses" to identify the smallest change in the robot state that makes a task, currently not solvable by the robot, solvable -- as a means to solicit more targeted demonstrations from a human. These excuses are generated automatically using combinatorial search over possible changes that can be made to the robot's state and choosing the minimum changes that make it solvable. These excuses then serve as guidance for the demonstrator who can use it to decide what to demonstrate to the robot in order to make this requested change possible, thereby making the original task solvable for the robot without having to demonstrate it in its entirety. By working with symbolic state descriptions, the excuses can be directly communicated and intuitively understood by a human demonstrator. We show empirically and in a user study that the use of excuses reduces the demonstration time by 54% and leads to a 74% reduction in demonstration size.

翻译：通过示教向机器人传授任务级指令是一种常用方法，可扩展机器人与环境交互的能力。然而，当前基于示教的学习系统主要侧重于将任务级知识抽象给机器人，缺乏理解机器人已有知识中可解决的任务部分的能力。因此，这些系统需要演示完整任务而非仅演示缺失环节，导致示教过程繁琐重复，可能阻碍人们通过示教协助机器人。为此，我们提出利用"借口"概念，识别使当前机器人无法解决的任务变为可解的最小状态变化——以此作为向人类获取更具针对性示教的手段。这些借口通过对机器人状态的可能变更进行组合搜索自动生成，并选择使任务可解的最小变更。生成的借口可为示教者提供指导，使其据此决定应向机器人示教何种内容以实现所需变更，从而无需完整演示即可使机器人解决原始任务。通过采用符号化状态描述，借口可直接传达并被人类示教者直观理解。实验与用户研究结果表明，使用借口可将示教时间减少54%，示教规模降低74%。