Sharing a story alongside an expository response is inherently human, often enhancing communication by adding personal details based on our unique experiences to what we say. When used in a task environment, narratives may be used to exploit measurable effects, such as on memory recall or interaction engagement. With the increasing presence of social robots in everyday environments, it remains unclear whether narrative communication from robots (e.g. “This picture shows a family who recently...”) instead of a factual description yields similar benefits to those observed in human-human interactions. In this paper, we develop and study a direction-giving robot, comparing three styles of navigation instruction: narrative with landmarks, landmarks only, and baseline without landmarks. We evaluate the effects of these conditions on recall, task success, and social acceptability factors (N=38) using a Furhat robot receptionist in a lab environment.

The present study describes the process employed to identify a suitable intelligent virtual agent (IVA) for the AMPER App supporting reminiscence therapy for those living with dementia through the use of a facilitating agent. This included three distinct phases: 1) co-creation with project stakeholders and identification of a set of desirable IVA traits; 2) a blind internal team rating process to select a subset from available IVAs; 3) a character survey with healthy older adults to select a final 4 IVAs (2 male, 2 female). We analyse the results, assess inter-subject agreement, and suggest guidelines for IVA selection.

We present a demonstration of the AMPER system: an Android application that guides individuals with Alzheimer’s disease and their carers through reminiscence therapy with the use of a virtual agent. The application supports a novel method of reminiscence story selection, using material metadata, user data, and a spreading activation algorithm. This aims to present relevant material to the user, in an order that attempts to mimic a human autobiographical memory.

Studying Human-Robot Interaction over time can provide insights into what really happens when a robot becomes part of people’s everyday lives. “In the Wild” studies inform the design of social robots, such as for the service industry, to enable them to remain engaging and useful beyond the novelty effect and initial adoption. This paper presents an “In the Wild” experiment where we explored the evolution of interaction between users and a Robo-Barista. We show that perceived trust and prior attitudes are both important factors associated with the usefulness, adaptability and likeability of the Robo-Barista. A combination of interaction features and user attributes are used to predict user satisfaction. Qualitative insights illuminated users’ Robo-Barista experience and contribute to a number of lessons learned for future long-term studies.

We present a demonstration of a Robo-Barista: a social robot that takes hot beverage orders through verbal interaction and completes them via a Bluetooth enabled coffee machine. The demonstration is highly robust and it is the intention that this could be installed as a permanent feature, enabling “In the Wild” experimentation and long term studies. In the demonstration video, we show a user interacting with a Furhat robot to order a coffee. The robot has a novel architecture that allows it to exhibit both verbal and non-verbal cues, such as shared attention and chitchat. Furthermore, it is enabled with a unique tiredness detector based on visual facial features.

As robots take on roles in our society, it is important that their appearance, behaviour and personality are appropriate for the job they are given and are perceived favourably by the people with whom they interact. Here, we provide an extensive quantitative and qualitative study exploring robot personality but, importantly, with respect to individual human traits. Firstly, we show that we can accurately portray personality in a social robot, in terms of extroversion-introversion using vocal cues and linguistic features. Secondly, through garnering preferences and trust ratings for these different robot personalities, we establish that, for a Robo-Barista, an extrovert robot is preferred and trusted more than an introvert robot, regardless of the subject’s own personality. Thirdly, we find that individual attitudes and predispositions towards robots do impact trust in the Robo-Baristas, and are therefore important considerations in addition to robot personality, roles and interaction context when designing any human-robot interaction study.

We focus on affective architecture issues relating to the generation of expressive facial behaviour, critique approaches that treat expressive behaviour as only a mirror of internal state rather than as also a social signal and discuss the advantages of combining the two approaches. Using the FAtiMA architecture, we analyse the requirements for generating expressive behavior as social signals at both reactive and cognitive levels. We discuss how facial expressions can be generated in a dynamic fashion. We propose generic architectural mechanisms to meet these requirements based on an explicit mind-body loop and Theory of Mind (ToM) processing. A illustrative scenario is given.

We present a study investigating the expressiveness of two different types of robots in a tutoring task. The robots used were i) the EMYS robot, with facial expression capabilities, and ii) the NAO robot, without facial expressions but able to perform expressive gestures. Preliminary results show that the NAO robot was perceived to be more friendly, pleasant and empathic than the EMYS robot as a tutor in a learning environment.

This article describes how an experimental platform for social, mobile and ubiquitous computing has been used in a wide-ranging longitudinal “in the wild” case study of the platform with a set of third-party services. The article outlines some of the relevant aspects of the platform, including built-in support for community formation, for context sensitivity, automated learning and adaptation to the user, and for management of privacy and trust relationships. The platform architecture is based on the notion of Cooperating Smart Spaces (CSSs), where a CSS is a partition of the platform corresponding to a single user and distributed over the devices belonging to that user. Three of the case study services were intended for use in a physical environment specifically created to support ubiquitous intelligence; they were highly interactive and used shared screens, voice input and gestural interaction. Another three ubiquitous services were available throughout the university environment as mobile and desktop services. The case study exploited this architecture's ability to integrate multiple novel applications and interface devices and to deliver them flexibly in these different environments. The platform proved to be stable and reliable and the study shows that treating a provider of services and resources (the University) as a CSS is instrumental in enabling the platform to provide this range of services across differing environments.

In this paper, we present an approach to monitor human activities such as entry, exit and break times of people in a workplace environment. The companion robot then learns the users' presence patterns over a period of time through memory generalisation and plans a suitable time for re charging itself causing less hindrance to human-robot interaction.

This work proposes an initial memory model for a long-term artificial companion, which migrates among virtual and robot platforms based on the context of interactions with the human user. This memory model enables the companion to remember events that are relevant or significant to itself or to the user. For other events which are either ethically sensitive or with a lower long-term value, the memory model supports forgetting through the processes of generalisation and memory restructuring. The proposed memory model draws inspiration from the human short-term and long-term memories. The short-term memory will support companions in focusing on the stimuli that are relevant to their current active goals within the environment. The long-term memory will contain episodic events that are chronologically sequenced and derived from the companion's interaction history both with the environment and the user. There are two key questions that we try to address in this work: 1) What information should the companion remember in order to generate appropriate behaviours and thus smooth the interaction with the user? And, 2) What are the relevant aspects to take into consideration during the design of memory for a companion that can have different types of virtual and physical bodies? Finally, we show an implementation plan of the memory model, focusing on issues of information grounding, activation and sensing based on specific hardware platforms.

The MY virtual graffiti (MYVG) system is an artistic medium that uses two sensor gloves, a six degree of freedom hand-held controller (Space Orb 360/spl deg/), a tracking system and dummy spray can nozzle strokes to create graffiti in a fully immersive CAVE environment. It redefines the traditional communication and art form of graffiti using virtual reality technology. Realistic paint splat is simulated directly on 3D objects in the virtual world. The paper describes the interaction devices, spray paint simulation algorithm, 3D colour and nozzle picker and the intuitive user interface for creating and storing graffiti. Users' evaluation result along with some sample graffiti are presented.