Learning from Demonstration (LfD) systems are commonly used to teach robots new tasks by generating a set of skills from user-provided demonstrations. These skills can then be sequenced by planning algorithms to execute complex tasks. However, LfD systems typically require a full demonstration of the entire task, even when parts of it are already known to the robot. This limitation comes from the system's inability to recognize which sub-tasks are already familiar, leading to a repetitive and burdensome demonstration process for users. In this paper, we introduce a new method for guided demonstrations that reduces this burden, by helping the robot to identify which parts of the task it already knows, considering the overall task goal and the robot's existing skills. In particular, through a combinatorial search, the method finds the smallest necessary change in the initial task conditions that allows the robot to solve the task with its current knowledge. This state is referred to as the excuse state. The human demonstrator is then only required to teach how to reach the excuse state (missing sub-task), rather than demonstrating the entire task. Empirical results and a pilot user study show that our method reduces demonstration time by 61% and decreases the size of demonstrations by 72%.

翻译：示教学习系统通常通过从用户提供的演示中生成一组技能来教导机器人执行新任务。这些技能随后可通过规划算法进行排序，以执行复杂任务。然而，示教学习系统通常需要完整的任务演示，即使机器人已掌握部分任务环节。这一局限性源于系统无法识别哪些子任务已为机器人所熟知，导致用户需要重复进行繁琐的演示过程。本文提出一种新的引导式演示方法，通过帮助机器人结合总体任务目标与现有技能识别已掌握的任务环节，从而减轻用户负担。该方法通过组合搜索，找到初始任务条件的最小必要变更，使机器人能够运用现有知识完成任务。此状态被称为"借口状态"。演示者仅需教导如何达到借口状态（即缺失的子任务），而无需演示完整任务。实证结果与初步用户研究表明，该方法使演示时间减少61%，演示规模缩减72%。