Learning from Demonstration (LfD) is a framework that allows lay users to easily program robots. However, the efficiency of robot learning and the robot's ability to generalize to task variations hinges upon the quality and quantity of the provided demonstrations. Our objective is to guide human teachers to furnish more effective demonstrations, thus facilitating efficient robot learning. To achieve this, we propose to use a measure of uncertainty, namely task-related information entropy, as a criterion for suggesting informative demonstration examples to human teachers to improve their teaching skills. In a conducted experiment (N=24), an augmented reality (AR)-based guidance system was employed to train novice users to produce additional demonstrations from areas with the highest entropy within the workspace. These novice users were trained for a few trials to teach the robot a generalizable task using a limited number of demonstrations. Subsequently, the users' performance after training was assessed first on the same task (retention) and then on a novel task (transfer) without guidance. The results indicated a substantial improvement in robot learning efficiency from the teacher's demonstrations, with an improvement of up to 198% observed on the novel task. Furthermore, the proposed approach was compared to a state-of-the-art heuristic rule and found to improve robot learning efficiency by 210% compared to the heuristic rule.

翻译：从示教中学习（LfD）是一种允许非专业用户轻松编程机器人的框架。然而，机器人学习的效率及其对任务变体进行泛化的能力，在很大程度上取决于所提供示教的质量和数量。我们的目标是引导人类教师提供更有效的示教，从而促进机器人高效学习。为实现这一目标，我们提出使用一种不确定性度量——即任务相关信息熵——作为标准，向人类教师建议信息量丰富的示教示例，以提升其教学技能。在开展的一项实验（N=24）中，我们采用基于增强现实（AR）的引导系统来训练新手用户，使其在操作空间内熵值最高的区域生成额外的示教。这些新手用户经过几次尝试训练后，使用有限数量的示教教会机器人一项可泛化的任务。随后，在无引导条件下，分别评估用户在相同任务（保持测试）和新任务（迁移测试）上的表现。结果表明，机器人从教师示教中学习的效率显著提升，在新任务上观察到的改进幅度高达198%。此外，将所提出的方法与一种最新启发式规则进行比较发现，相较于该启发式规则，机器人学习效率提高了210%。