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Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment
Robotics · Submitted 25 Aug 2026
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Learning from demonstration (LfD) methods enable non-expert end users to teach robots novel skills without explicit programming. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s1 | However most evaluations of the usability of LfD with non-experts has been conducted in controlled laboratory environments with a robotics experimenter present. | gap 0.92 | 0.06 | 0.03 | asserted 1.00 |
| s2 | 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. | contribution 0.93 | 0.89 | 0.11 | asserted 0.97 |
| s3 | 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. | method 0.94 | 0.74 | 0.06 | asserted 0.99 |
| s4 | Towards the ecological validity of the evaluation, we conduct this experimentation over multiple visits, with a population of care providers. | method 0.95 | 0.94 | 0.05 | asserted 0.99 |
| s5 | 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. | contribution 0.63 | 0.68 | 0.11 | asserted 0.81 |
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The paper
Original 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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