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arXiv · 2608.25196

Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment

Nina Moorman·Julianna Schalkwyk·Vriksha Srihari·Qingyu Xiao·Kamel Alrashedy·Hongseok Jeong·Kiersten Lange·Matthew B. Luebbers·Matthew Gombolay
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Abstract

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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Nina Moorman, Julianna Schalkwyk, Vriksha Srihari, Qingyu Xiao, Kamel Alrashedy, Hongseok Jeong, Kiersten Lange, Matthew B. Luebbers, Matthew Gombolay. 2026-08-25. Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment. https://arxiv.org/abs/2608.25196

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