In-situ learning from a domain expert for real world socially assistive robot deployment

Winkle, Katie; Lemaignan, Séverin; Caleb-Solly, Praminda; Bremner, Paul; Turton, Ailie; Leonards, Ute

Abstract

The effectiveness of Socially Assistive Robots (SAR) relies on their ability to motivate particular user behaviours, e.g. engagement with a task, requiring complex social interactions tailored to the needs and motivations of the user. Professionals from human-centred domains such as healthcare are experts in such interactions, but their ability to contribute to SAR development has traditionally been limited to the identification of applications and key design requirements. In this work we demonstrate how interactive machine learning offers a way for such experts to be involved at every stage of design and automation of a robot, as well as the value of taking this approach. We present a novel technical framework for in-situ, online interactive machine learning that can be used in ecologically-valid human-robot interactions. Using this framework, we were able generate fully autonomous, appropriate and personalised robot behaviour in a high-dimensional application of assistive robotics.

Citation

Winkle, K., Lemaignan, S., Caleb-Solly, P., Bremner, P., Turton, A., & Leonards, U. (2020). In-situ learning from a domain expert for real world socially assistive robot deployment. . https://doi.org/10.15607/RSS.2020.XVI.059

• Conference Name: Robotics: Science and Systems
• Conference Location: Corvalis, Oregon, USA
• Start Date: Jul 12, 2020
• End Date: Jul 16, 2020
• Publication Date: Jan 1, 2020
• Deposit Date: Jan 26, 2024
• ISBN: 9780992374761
• DOI: https://doi.org/10.15607/RSS.2020.XVI.059
• Public URL: https://uwe-repository.worktribe.com/output/11628599