让照护人员在家中教机器人新技能，验证无专家指导下的教学可行性。
Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment
- 通过预训练和自适应反馈支持非专家教学
- 多轮访问实验发现关键障碍在于操作信心与反馈延迟
- 首次公开家庭环境中照护者教学的长期数据集
学习从示范（LfD）方法使非专家用户无需编程即可教会机器人新技能。然而，目前大多数关于非专家使用LfD的评估都在有机器人专家在场的受控实验室环境中进行。本文研究非专家在无实时机器人专家反馈的家庭环境中教学时面临的关键障碍。通过多轮访问的人类被试实验，我们采用先前工作中开发的两种示范者引导方式：预训练和自适应反馈。为提高评估的生态有效性，实验对象为照护人员群体。最后，我们提议开源该数据集，记录照护人员在家庭环境中多次访问下教授机器人辅助任务的全过程。
原文摘要 · Abstract (English)
Learning from demonstration (LfD) methods enable non-expert end users to teach robots novel skills without explicit programming. However most evaluations of the usability of LfD with non-experts has been conducted in controlled laboratory environments with a robotics experimenter present. In this work we identify non-expert end users' key barriers when teaching robots via demonstration without live robotics expert feedback in a home environment. In our human subjects experiment we support the non-expert end users through two forms of demonstrator guidance developed in prior work: pre-training and adaptive feedback. Towards the ecological validity of the evaluation, we conduct this experimentation over multiple visits, with a population of care providers. Finally, we propose to open source the resulting LfD dataset of care providers teaching a robot assistive tasks over multiple visits to a home environment.
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