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When robot personalisation does not help: Insights from a robot-supported learning study

Conference paper
Authors Alex Gao
Wolmet Barendregt
Mohammad Obaid
Ginevra Castellano
Published in Proceedings of RO-MAN'. 27th International Symposium on Robot and Human Interactive Communication
ISBN 978-1-5386-7981-4
Publisher IEEE
Publication year 2018
Published at Department of Applied Information Technology (GU)
Language en
Keywords personalisation; robotics; Reinforcement learning
Subject categories Computer Vision and Robotics (Autonomous Systems), Human Computer Interaction, Robotics


In the domain of robotic tutors, personalised tutoring has started to receive scientists’ attention, but is still relatively underexplored. Previous work using rein- forcement learning (RL) has addressed personalised tutor- ing from the perspective of affective policy learning. How- ever, little is known about the effects of robot behaviour personalisation on user’s task performance. Moreover, it is also unclear if and when personalisation may be more ben- eficial than a robot that adapts to its users and the context of the interaction without personalising its behaviour. In this paper we build on previous work on affective policy learning that used RL to learn what robot’s supportive behaviours are preferred by users in an educational sce- nario. We build a RL framework for personalisation that allows a robot to select verbal supportive behaviours to maximise the user’s task progress and positive reactions in a learning scenario where a Pepper robot acts as a tutor and helps people to learn how to solve grid-based logic puzzles. A between-subjects design user study showed that participants were more efficient at solving logic puzzles and preferred a robot that exhibits more varied behaviours compared with a robot that personalises its behaviour by converging on a specific one over time. We discuss insights on negative effects of personalisation and report lessons learned together with design implications for personalised robots.

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