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.

Sharing casual stories in conversations is a natural human behaviour that adds personal and unique details, while also offering measurable benefits such as improved memory recall and more positive social human-human interactions. However, as social robots become more common in everyday environments, it is unclear whether these stories, or narratives, have similar effects on human-robot interactions with embodied agents.

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.