Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This study investigates the challenges faced by non-expert users, specifically care providers, when teaching robots through Learning from Demonstration (LfD) in home settings without expert support. By implementing two forms of demonstrator guidance—pre-training and adaptive feedback—over multiple visits, the research highlights key barriers and enhances the usability of LfD for non-experts. The resulting open-source dataset captures the interactions of care providers teaching robots assistive tasks, providing valuable insights for future LfD applications in real-world environments.
Non-expert care providers face significant barriers in teaching robots, but targeted guidance can dramatically improve their effectiveness in real-world settings.
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.