发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对居家环境中无专家反馈时非专业照护者教机器人的障碍，采用预训练与自适应反馈开展多轮人体实验，拟开源相关LfD辅助任务数据集。
AI 中文摘要
演示学习（Learning from demonstration，LfD）方法使非专业终端用户无需明确编程即可向机器人传授新技能。然而，多数针对非专业用户使用LfD的可用性评估是在受控实验室环境中开展的，且有机器人专家在场。本研究确定了非专业终端用户在居家环境中无现场机器人专家反馈时，通过演示教机器人的关键障碍。在人体实验中，我们通过两种前期开发的演示者指导形式为非专业终端用户提供支持：预训练和自适应反馈。为提升评估的生态效度，我们开展了多轮次实验，研究对象为照护者群体。最后，我们提议将由此产生的照护者在多轮居家访问中向机器人传授辅助任务的LfD数据集开源。
英文摘要
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.
CommentsICRA 2026 Workshop on Bridging the Gap between Robot Learning and Human-Robot Interaction
