Enhancing the expressiveness of human teaching is vital for both improving robots' learning from humans and the human-teaching-robot experience. In this work, we characterize and test a little-used teaching signal: \textit{progress}, designed to represent the completion percentage of a task. We conducted two online studies with 76 crowd-sourced participants and one public space study with 40 non-expert participants to validate the capability of this progress signal. We find that progress indicates whether the task is successfully performed, reflects the degree of task completion, identifies unproductive but harmless behaviors, and is likely to be more consistent across participants. Furthermore, our results show that giving progress does not require extra workload and time. An additional contribution of our work is a dataset of 40 non-expert demonstrations from the public space study through an ice cream topping-adding task, which we observe to be multi-policy and sub-optimal, with sub-optimality not only from teleoperation errors but also from exploratory actions and attempts. The dataset is available at https://github.com/TeachingwithProgress/Non-Expert\_Demonstrations.

翻译：提升人类教学的表达能力对于改进机器人向人类学习的能力以及人机教学体验都至关重要。在本研究中，我们描述并测试了一种较少使用的教学信号：\textit{进度}，该信号旨在表示任务的完成百分比。我们进行了两项涉及76名众包参与者的在线研究以及一项涉及40名非专业参与者的公共空间研究，以验证此进度信号的有效性。我们发现，进度信号能够指示任务是否成功执行、反映任务完成程度、识别无益但无害的行为，并且很可能在不同参与者间具有更高的一致性。此外，我们的结果表明，提供进度信号并不需要额外的工作负担和时间。本研究的另一项贡献是，通过一项冰淇淋添加配料任务，我们从公共空间研究中收集了一个包含40个非专业演示的数据集。我们观察到这些演示具有多策略性和次优性，其次优性不仅源于遥操作误差，还源于探索性动作和尝试。该数据集可在 https://github.com/TeachingwithProgress/Non-Expert\_Demonstrations 获取。