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

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--> [Submitted on 25 Aug 2026] Title:Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment View a PDF of the paper titled Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment, by Nina Moorman and 8 other authors View PDF HTML (experimental) 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. Comments: ICRA 2026 Workshop on Bridging the Gap between Robot Learning and Human-Robot Interaction Subjects: Robotics (cs.RO); Human-Computer Interaction (cs.HC) Cite as: arXiv:2608.25196 [cs.RO] (or arXiv:2608.25196v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.25196 arXiv-issued DOI via DataCite (pending registration) Submission history From: Nina Moorman [view email] [v1] Tue, 25 Aug 2026 22:21:09 UTC (858 KB) Full-text links: Access Paper: View a PDF of the paper titled Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment, by Nina Moorman and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-08 Change to browse by: cs cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)