This paper examines participants' experiences of interacting with a robotic companion (agent) that has the ability to move its "mind" between different robotic embodiments to take advantage of the features and functionalities associated with the different embodiments in a process called agent migration. In particular, we focus on identifying factors that can help the companion retain its identity in different embodiments. This includes examining the clarity of the migration behaviour and how this behaviour may contribute to identity retention. Nine participants took part in a long-term study, and interacted with the robotic companion in the smart house twice-weekly over a period of 5 weeks. We used Narrative-based Integrated Episodic Scenario (NIES) framework for designing long-term interaction scenarios that provided habituation and intervention phases while conveying the impression of continuous long-term interaction. The results show that NEIS allows us to explore complex intervention scenarios and obtain a sense of continuity of context across the long-term study. The results also suggest that as participants become habituated with the companion, they found the realisation of migration signaling clearer, and felt more certain of the identity of the companion in later sessions, and that the most important factor for this was the agent's continuation of tasks across embodiments. This paper is both empirical as well as methodological in nature.

In order to investigate how the use of robots may impact everyday tasks, twelve participants in our study interacted with a University of Hertfordshire Sunflower robot over a period of 8 weeks in the university's Robot House. Participants performed two constrained tasks, one physical and one cognitive, four times over this period. Participant responses were recorded using a variety of measures including the System Usability Scale and the NASA Task Load Index. The use of the robot had an impact on the experienced workload of the participants differently for the two tasks, and this effect changed over time. In the physical task, there was evidence of adaptation to the robot's behavior. For the cognitive task, the use of the robot was experienced as more frustrating in the later weeks.

This article describes the prototyping of human-robot interactions in the University of Hertfordshire (UH) Robot House. Twelve participants took part in a long-term study in which they interacted with robots in the UH Robot House once a week for a period of 10 weeks. A prototyping method using the narrative framing technique allowed participants to engage with the robots in episodic interactions that were framed using narrative to convey the impression of a continuous long-term interaction. The goal was to examine how participants responded to the scenarios and the robots as well as specific robot behaviours, such as agent migration and expressive behaviours. Evaluation of the robots and the scenarios were elicited using several measures, including the standardised System Usability Scale, an ad hoc Scenario Acceptance Scale, as well as single-item Likert scales, open-ended questionnaire items and a debriefing interview. Results suggest that participants felt that the use of this prototyping technique allowed them insight into the use of the robot, and that they accepted the use of the robot within the scenario.

This book chapter describes the implementation, testing, and evaluation of the first prototype of the “AIBOcom” system, which allows remote users to play an interactive game cooperatively each using a pet-like robot as a social mediator. An exploratory pilot study tested this remote communication system with 10 pairs of participants who were exposed to two experimental conditions characterised by two different modes of synchronisation between the two robots that each interacts locally with the participant. In one mode, the robots incrementally affected each other’s behaviour, while in the other, the robots mirrored each other’s behaviour. Instruments used in this study include questionnaires, video observations and log files for the game state. The authors used various techniques to measure engagement and synchronization such as quantitative (e.g. rate of occurrence and average values) as well as qualitative measurements. In an exploratory data analysis, these multiple sources of data reflecting participant performance and characteristics were analyzed. Significant correlations were found and presented between the participants as well as participants’ preferences and overall acceptance of such communication media. Findings indicate that participants preferred the mirroring mode, and that in this pilot study, robot-assisted remote communication was considered desirable and acceptable to the participants. Furthermore, the existence of interaction variations among different demographic groups was found, while this chapter lists and interprets the most significant effects.

This article describes the design and evaluation of AIBOStory - a novel, remote interactive story telling system that allows users to create and share common stories through an integrated, autonomous robot companion acting as a social mediator between two remotely located people. The behaviour of the robot was inspired by dog behaviour, including a simple computational memory model. AIBOStory has been designed to work alongside online video communication software and aims to enrich remote communication experiences over the internet. An initial pilot study evaluated the proposed system’s use and acceptance by the users. Five pairs of participants were exposed to the system, with the robot acting as a social mediator, and the results suggested an overall positive acceptance response. The main study involved long-term interactions of 20 participants using AIBOStory in order to study their preferences between two modes: using the game enhanced with an autonomous robot and a non-robot mode which did not use the robot. Instruments used in this study include multiple questionnaires from different communication sessions, demographic forms and logged data from the robots and the system. The data was analysed using quantitative and qualitative techniques to measure user preference and human-robot interaction. The statistical analysis suggests user preferences towards the robot mode.

This paper examines the role of spatial behaviours in building human-robot relationships. A group of 8 participants, involved in a long-term HRI study, interacted with an artificial agent using different embodiments over a period of one and a half months. The robot embodiments had similar interactional and expressive capabilities, but only one embodiment was capable of moving. Participants reported feeling closer to the robot embodiment capable of physical movement and rated it as more likable. Results suggest that while expressive and communicative abilities may be important in terms of building affinity and rapport with human interactants, the importance of physical interactions when negotiating shared physical space in real time should not be underestimated.

Our long-term goal is to develop robots as social mediators that can support human-human communication in remote interaction scenarios. This paper explores the effects of an autonomous robot on human-human remote communication and studies participants’ preferences in comparison with a communication system not involving robots. We developed a platform for remote human-human communication in the context of a collaborative computer game. The exploratory study involved twenty pairs of participants who communicated using video conference software. Participants expressed more social cues when using the robot and sharing of their game experiences with each other. However, analyses of the interactions of the participants with each other and with the robot show that it is difficult for participants to familiarise themselves quickly with the robot while they can perform the same task more efficiently with conventional devices. These issues need to be carefully considered and addressed when designing human-human remote communication systems with robots as social mediators.

To enable intelligent agents interacting smoothly with human users, researchers have been deploying novel interaction modalities (e.g. non-verbal cue, vision and touch) in addition to agents’ conversational skills. Models of multi-modality interaction can enhance agents’ real-time perception, cognition and reaction towards the user. In this paper we report a novel tele-immersive interaction system developed using real-time 3D modelling techniques. In such system user’s full body is reconstructed using multi-view cameras and CUDA based visual hull reconstruction algorithm. User’s mesh model is then loaded into a virtual environment for interacting with an autonomous agent. Technical details and initial results of the system are illustrated in this paper. Following that a novel interaction scenario is proposed which links the virtual agent with a remote physical robot who takes the role of mediating interactions between two geographically separated users. Finally we discuss in depth the implications of such human-agent interaction and possible future improvements and directions.

This paper demonstrates a biologically- and psychologicallyinspired human-like computational memory focusing on the retrieval mechanisms { Spreading Activation and Compound Cue for a companion agent’s episodic memory that might help the agent to manage it’s memory more eciently and enable it to have a more natural interaction with the user.

This research investigates event generalisation in computational episodic memory for artificial companions. Two studies indicated a preference of a biologically-inspired selective memory over an absolute memory companion. Consequently, we present a preliminary implementation of a forgetting mechanism that enables the companion to create "generalised event representations" from its experiences allowing the companion to learn from past encounters.

This paper considers the ethical implications of applying three major ethical theories to the memory structure of an artificial companion that might have different embodiments such as a physical robot or a graphical character on a hand-held device. We start by proposing an ethical memory model and then make use of an action-centric framework to evaluate its ethical implications. The case that we discuss is that of digital artefacts that autonomously record and store user data, where this data are used as a resource for future interaction with users.

The idea of a robot as a long-term companion has not been widely accepted and sometimes not even considered or imagined. Hence, several issues should be addressed in order to facilitate this long-term human-robot interaction. This work focuses on the memory modelling for artificial companions and its forgetting processes by proposing a novel approach based on hierarchical classification data mining techniques to implement memory generalisation mechanisms.

In the recent decades, improved quality of life and medical technology advancement have led to extension in life expectancy. However, neurological changes are commonly observed in the aging population – they are very often associated with gradual degenerative alteration in brain function, including Alzheimer’s and Parkinson’s diseases, as well as healthy aging. Recently, an increasing number of people with Alzheimer’s disease and dementia has been reported, leading to an increase in the demand for long term care facilities and staff. In order to tackle this problem, our research focuses on the creation of artificial companions with humanlike memory that can generate natural interaction with the user and maintain his/her memory function in a way comparable to cognitive training approaches in conjunction to the Healthcare and Quality of Life themes of the Digital Economy.

This paper investigates issues of robot's personalization and long-term adaptation in human-robot interaction (HRI). It demonstrates the design and first technical implementation of a HRI showcase in the Robot House at University of Hertfordshire, UK. Here the central idea facilitating the long-term HRI is the creation of robotic companion, which provides various types of service to the user and can be personalised based upon individual needs. The personalisation can also be further enhanced through repeated interactions. The key component in the "mind" of the companion, which is highlighted in this paper, is the model of human semantic and episodic memory. The memory not only allows the companion to remember user's preferences for practical daily tasks, it also changes companion's behaviour in a longer time scale based on robot's perception of actual user input. Finally, implications of such a memory model in HRI are discussed.