Robotics: Science and Systems XVI

In-Situ Learning from a Domain Expert for Real World Socially Assistive Robot Deployment

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


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.



    AUTHOR    = {Katie Winkle AND Severin Lemaignan AND Praminda Caleb-Solly AND Paul Bremner AND Ailie Turton AND Ute Leonards}, 
    TITLE     = {{In-Situ Learning from a Domain Expert for Real World Socially Assistive Robot Deployment}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2020}, 
    ADDRESS   = {Corvalis, Oregon, USA}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2020.XVI.059}