Lee J. Corrigan

Empathic robotic tutors for personalised learning : A multidisciplinary approach

Within any learning process, the formation of a socio-emotional relationship between learner and teacher is paramount to facilitating a good learning experience. The ability to form this relationship may come naturally to an attentive teacher; but how do we endow an unemotional robot with this ability? In this paper, we extend upon insights from the literature to include tools from user-centered design (UCD) and analyses of human-human interaction (HHI) as the basis of a multidisciplinary approach in the development of an empathic robotic tutor. We discuss the lessons learned in respect to design principles with the aim of personalised learning with empathic robotic tutors.

Developing interactive embodied characters using the thalamus framework: A collaborative approach

We address the situation of developing interactive scenarios featuring embodied characters that interact with users through various types of media easily presents as a challenge. Some of the problems that developers face are on collaborating while developing remotely, integrating all the independently developed components, and incrementally developing a system in such way that the developed components can be used since their incorporation, throughout the intermediate phases of development, and on to the final system. We describe how the Thalamus framework addresses these issues, and how it is being used on a large project that targets developing this type of scenarios. A case study is presented, illustrating actual development of such scenario which was then used for a Wizard-of-Oz study.

Mixing implicit and explicit probes: finding a ground truth for engagement in social human-robot interactions

In our work we explore the development of a computational model capable of automatically detecting engagement in social human-robot interactions from real-time sensory and contextual input. However, to train the model we need to establish ground truths of engagement from a large corpus of data collected from a study involving task and social-task engagement. Here, we intend to advance the current state-ofthe-art by reducing the need for unreliable post-experiment questionnaires and costly time-consuming annotation with the novel introduction of implicit probes. A non-intrusive, pervasive and embedded method of collecting informative data at different stages of an interaction.

Engagement Perception and Generation for Social Robots and Virtual Agents

Technology is the future , woven into every aspect of our lives, but how are we to interact with all this technology and what happens when problems arise? Artificial agents, such as virtual characters and social robots could offer a realistic solution to help facilitate interactions between humans and machines—if only these agents were better equipped and more informed to hold up their end of an interaction. People and machines can interact to do things together, but in order to get the most out of every interaction, the agent must to be able to make reasonable judgements regarding your intent and goals for the interaction. We explore the concept of engagement from the different perspectives of the human and the agent. More specifically, we study how the agent perceives the engagement state of the other interactant, and how it generates its own representation of engaging behaviour. In this chapter, we discuss the different stages and components of engagement that have been suggested in the literature from the applied perspective of a case study of engagement for social robotics, as well as in the context of another study that was focused on gaze-related engagement with virtual characters.

Identifying Task Engagement: Towards Personalised Interactions with Educational Robots

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.

Empathic Robotic Tutors: Map Guide

In this demonstration we describe a scenario developed in the EMOTE project. The overall goal of the project is to develop an empathic robot tutor for 11-13 year old school students in an educational setting. We are aiming to develop an empathic robot tutor to teach map reading skills with this scenario on a touch-screen device.

The Empathic Robotic Tutor: Featuring the NAO Robot

We present an autonomous empathic robotic tutor to be used in classrooms as a peer in a virtual learning environment. The system merges a virtual agent design with HRI features, consisting of a robotic embodiment, a multimedia interactive learning application and perception sensors that are controlled by an artificial intelligence agent.