日常用户如何通过示范高效地教导机器人？
How Can Everyday Users Efficiently Teach Robots by Demonstrations?
AI总结：
本文提出利用任务相关信息熵作为不确定性度量，通过AR系统指导新手用户提供高信息熵示范，显著提升了机器人学习效率及在新任务上的泛化能力。
摘要：
从示范中学习（LfD）是一个允许非专业用户轻松对机器人进行编程的框架。然而，机器人学习的效率及其对任务变化的泛化能力取决于所提供示范的质量和数量。我们的目标是指导人类教师提供更有效的示范，从而促进高效的机器人学习。为此，我们提出使用一种不确定性度量，即任务相关信息熵，作为向人类教师建议信息丰富的示范示例的标准，以提高他们的教学技能。在一项实验（N=24）中，采用基于增强现实（AR）的指导系统训练新手用户，使其在工作空间中信息熵最高的区域生成额外的示范。这些新手用户接受了少量试验的训练，以使用有限数量的示范来教导机器人完成一项可泛化的任务。随后，在没有指导的情况下，首先评估用户在相同任务上的训练后表现（保留），然后评估在新任务上的表现（迁移）。结果表明，从教师示范中进行的机器人学习效率有显著提升，在新任务上观察到高达198%的提升。此外，将所提出的方法与一种最先进的启发式规则进行比较，发现与该启发式规则相比，机器人学习效率提高了210%。
英文摘要：
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.
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。
