Affect state recognition for adaptive human robot interaction in learning environments

Previous studies of robots used in learning environments suggest that the interaction between learner and robot is able to enhance the learning procedure towards a better engagement of the learner. Moreover, intelligent robots can also adapt their behavior during a learning process according to certain criteria resulting in increasing cognitive learning gains. Motivated by these results, we propose a novel Human Robot Interaction framework where the robot adjusts its behavior to the affect state of the learner. Our framework uses the theory of flow to label different affect states (i.e., engagement, boredom and frustration) and adapt the robot's actions. Based on the automatic recognition of these states, through visual cues, our method adapt the learning actions taking place at this moment and performed by the robot. This results in keeping the learner at most times engaged in the learning process. In order to recognizing the affect state of the user a two step approach is followed. Initially we recognize the facial expressions of the learner and therefore we map these to an affect state. Our algorithm perform well even in situations where the environment is noisy due to the presence of more than one person and/or situations where the face is partially occluded

The interaction between voice and appearance in the embodiment of a robot tutor

Robot embodiment is, by its very nature, holistic and understanding how various aspects contribute to the user perception of the robot is non-trivial. A study is presented here that investigates whether there is an interaction effect between voice and other aspects of embodiment, such as movement and appearance, in a pedagogical setting. An on-line study was distributed to children aged 11–17 that uses a modified Godspeed questionnaire. We show an interaction effect between the robot embodiment and voice in terms of perceived lifelikeness of the robot. Politeness is a key strategy used in learning and teaching, and here an effect is also observed for perceived politeness. Interestingly, participants’ overall preference was for embodiment combinations that are deemed polite and more like a teacher, but are not necessarily the most lifelike. From these findings, we are able to inform the design of robotic tutors going forward

Influencing Hand-washing Behaviour with a Social Robot: HRI Study with School Children in Rural India

The work presented in this paper reports the influence of a social robot on hand washing behaviour on school children in rural India with a significant presence of indigenous tribes. We describe the design choices of our social robot to cater the requirements of the intervention. The custom built wall mounted social robot encouraged 100 children to wash their hand at appropriate time (before meal and after toilet) using the correct handwashing technique via a poster on a wall. The results indicate that the intervention using the robot was found to be effective (40% rise) at increasing levels of hand washing with soap and with a better handwashing technique in ecologically valid settings

Identifying Task Engagement: Towards Personalised Interactions with Educational Robots

Abstract-The focus of this project is to design, develop and evaluate a new computational model for automatically detecting change in task engagement. This work will be applied to robotic tutors to enhance and support the learning experience, enabling timely pedagogical and empathic intervention. This work is intended to forward the current state of the art by 1) exploring how to automatically detect engagement with a learning task, 2) designing and developing new approaches to machine learning for adaptive platform-independent modelling and 3) evaluation of its effectiveness for building and maintaining learner engagement across different tutor embodiments, for example a physical and virtual embodiment