Motivating older adults to engage in cognitive activities has the potential to slow cognitive decline. This article presents a participatory design (PD) workshop and follow-up prototype evaluation to determine how cognitive training activities can be adapted to integrate socially assistive robots and sensory feedback (visual, auditory, and haptic, specifically). The workshop with older adults and therapists resulted in concrete designs and strategies for engagement. The second phase of this work was to implement these outcomes into a prototype that incorporated a humanoid robot and sensory feedback, with a particular focus on haptic feedback. The evaluation with eight older adults supported the potential of hand tracking with sensory feedback as an interaction mechanism to foster engagement, where the increased workload notably led to high levels of engagement. The prototype results confirmed the strategies and designs from the PD workshop were effective as a way of engaging older adults in cognitive activities. This article highlights the potential for the unique combination of socially assistive robots and sensory feedback to promote older adults’ engagement in cognitive activities.

Social robots can support children in their socio-emotional development [38]. To improve the cooperation between a child and a social robot, a good relationship is vital. Self-disclosure is an essential element for building personal relationships. Yet, knowledge about the effects of self-disclosure in child-robot interactions is still lacking. To investigate effects of robot persona, child personality, and self-disclosure category on self-disclosure in child-robot interaction, we have conducted a field study at a science festival in which children had a conversation with a robot that either behaved human-like or robot-like. The results show a significant difference in the amount of self-disclosure (in conversation duration) between the two robot personas. Additionally, significant relationships were found between conscientiousness and extraversion and amount of self-disclosure (in word count). The participant disclosed significantly more about the category `Attitudes and Opinions’ than about ‘School’. Finally, a thematic analysis shows that the content of the conversations can be categorised in five plus one themes. Between robot personas, the content of the conversations did not differ in terms of conversation themes. However, in both conditions, we found that children generally feel comfortable sharing unpleasant experiences about present themes (such as COVID) in a first encounter with a robot.

With the aim of providing extra motivation to adhere to repetitive, individual sports training, this paper presents an autonomous robotic squash coach capable of high-level personalisation. The system was evaluated in person with 16 participants each conducting three 15-minute solo practice sessions. We compared a baseline, non-coaching robotic condition to two conditions in which the robot executed one of 12 different coaching policies, each of which was based on human coaching data. In one of the coaching conditions, the policy was selected based on categories for personalisation and in the other it was selected randomly among policies. The coaching policy conditions were found to be more enjoyable, more socially competent, and perceived as a more effective coach than the baseline.

Discrimination and bias are pressing issues of many AI and robotics applications. These outcomes may derive from limited datasets that do not fully represent society as a whole or from the AI scientific community's western-male configuration bias. Although being a pressing issue, understanding how robotic systems can replicate and amplify inequalities and injustice among underrepresented communities is still in its infancy among social science and technical communities. This workshop contributes to filling this gap by exploring the research question: What do diversity and inclusion mean in the context of Human-Robot Interaction (HRI)? Here, attention is directed to three different levels of HRI: the technical, the community, and the target user level. Overall, this workshop will focus on the idea that AI systems can be created to be more attuned to inclusive societal needs, respect fundamental rights, and represent contemporary values in modern societies by integrating diversity and inclusion considerations.

There is growing interest in psychological interventions using socially assistive robots to mitigate distress and pain in the pediatric population. This work seeks to address the deficit in understanding of what features and functionality young children and their parents desire to help with pain management by using co-design, a common approach to exploring participants' imaginations and gathering design requirements. To close this gap, we carried out a co-design workshop involving seven families (with children aged between 4–6 and their parents) to understand their expectations and design preferences for a robot designed for pain management in children. Data were collected from surveys, video and audio recordings, interviews, and field notes. We present the robot prototypes constructed during the workshops and derive several preferences of the children (e.g, zoomorphic shape, distractors and emotional expressions as behaviors). Additionally, we report methodological insights regarding the involvement of young children and their parents in the co-design process. Based on the findings of this co-design study, we discuss personalization as a possible design concept for future child-robot interaction development.

Cognitive training is effective at retaining cognitive function and delaying decline for typically ageing older adults, individuals with mild cognitive impairment, and persons with dementia. Technological resources can address limiting factors that inhibit engagement and access to this treatment. We investigated how a socially assistive robot-facilitated memory task with sensory feedback was received by older adults. The impact of unimodal and multimodal administration of auditory and haptic feedback using two robot embodiments (Pepper and Nao) was evaluated in terms of user performance, usability, and workload. In contrast to sensory feedback research, auditory feedback resulted in significantly higher task accuracy. This was, however, supported by previous work from neurological literature. Auditory feedback also received significantly higher usability, and this preference was validated by qualitative feedback from participants. Regardless of robotic embodiment, this study demonstrates an advantage for auditory feedback (over haptic and multimodal) in cognitive training activities for older adults.

Self-disclosures can be valuable and sensitive parts of the human-robot interaction. This paper investigates how far human's tendency to self-disclose depends on the topic of interaction, individual's personality and perceived robot identity (i.e., human-, robot- or animal-like). Robot's (Pepper) identity was shown in its self-disclosure, interaction behaviors (gestures, sound and voice), and ’’clothing". In an"in-the- wild" study at a science festival, 80 visitors interacted with one of these robot identities. When questioned by the robot, they disclosed more about their attitudes and opinions than about other categories. Significant correlations appeared between personality characteristics and the degree of self-disclosure, as well as differences in self-disclosure categories. The different robot identities showed no effects on disclosures.

Adherence to repetitive rehabilitation exercises is important in motor recovery after stroke. Similarly, repetitive solo practice exercises can improve the skill level of sports players. In both of these scenarios, regular human coaching has benefits, but in practice, the required training is often carried out alone, resulting in lowered adherence. This work presents a mixed methodology approach, novel in the context of designing for HRI, towards informing the design of a personalised robotic coach for stroke rehabilitation and squash. Using observations of human-human interactions, we first obtained action sequences of behaviours exhibited by coaches and physiotherapists. We then clustered these action sequences into behaviour graphs, with each graph representing a coaching policy usable for robotic control. Next we obtained coaches’ and physiotherapists’ reflections on the graphs’ applicability to the real world. Finally, we provide an explanation of how the policies visualised in these graphs could be used for robotic control.

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.

Social robots are being developed as a form of social skills training for individual's with an autism spectrum condition (ASC). Effective training will therefore require the social signals produced by a robot to be contingent with people's knowledge and expectations of social cognition and behaviour. Designing recognisable facial expressions is an important part of this challenge; ensuring interactions are more believable and motivating. This design process requires - amongst other factors - consideration of how culture and native language affects social signal processing. In this experiment participants offered a full-bodied robot (named 'Alyx') food items to which Alyx reacted autonomously, producing either an approving or disapproving expression. Participant's responded to these expressions (i.e. the robots social signals) by indicating whether Alyx liked or disliked the food. Task performance was examined both quantitatively (response time and accuracy) and qualitatively (participant's reactionary expressions). The results revealed significant cultural differences, as non-native English speakers were less accurate at interpreting expressions, but also a similar response trend between these groups. Qualitative analysis supported the notion that Alyx's expressions were not universally understood. These findings are discussed in the context of social skills training.

This paper outlines a demonstration of the work carried out in the SoCoRo project investigating how far a neuro-typical population recognises facial expressions on a non-naturalistic robot face that are designed to show approval and disapproval. RFID-tagged objects are presented to an Emys robot head (called Alyx) and Alyx reacts to each with a facial expression. Participants are asked to put the object in a box marked 'Like' or 'Dislike'. This study is being extended to include assessment of participants' Autism Quotient using a validated questionnaire as a step towards using a robot to help train high-functioning adults with an Autism Spectrum Disorder in social signal recognition.

In this paper we present the design and technical implementation of a web based Multi-Modal User Interface (MMUI) tailored for elderly users of the robotic services developed by the EU FP7 Large-Scale Integration Project Robot-Era. The project partners are working to significantly enhance the performance and acceptability of technological services for ageing well by delivering a fully realized system based on the cooperation of multiple heterogeneous robots and with the support of an Ambient Assisted Living environment. To this end, elderly users were involved in the definition of the services and in the design of the hardware and software of the robotic platforms from the first stages of the development process and in real experimentation in two test sites. In particular, here we detail the interface software system for multi-modal elderly-robot interaction. The MMUI is designed to run on any device including touch-screen mobiles and tablets that are preferred by the elderly. This is obtained by integrating web based solutions with the Robot-Era middlewares and planner. Finally we present some preliminary results of ongoing experiments to show the successful evaluation of usability by potential users and to discuss the future directions to improve the proposed MMUI software system.

We overview how sensorimotor experience can be operationalized for interaction scenarios in which humanoid robots acquire skills and linguistic behaviours via enacting a “form-of-life” in interaction games (following Wittgenstein) with humans. The enactive paradigm is introduced which provides a powerful framework for the construction of complex adaptive systems, based on interaction, habit, and experience. Enactive cognitive architectures (following insights of Varela, Thompson and Rosch) that we have developed support social learning and robot ontogeny by harnessing information-theoretic methods and raw uninterpreted sensorimotor experience to scaffold the acquisition of behaviours. The success criterion here is validation by the robot engaging in ongoing human-robot interaction with naive participants who, over the course of iterated interactions, shape the robot's behavioural and linguistic development. Engagement in such interaction exhibiting aspects of purposeful, habitual recurring structure evidences the developed capability of the humanoid to enact language and interaction games as a successful participant.

The purpose of this half-day workshop is to explore the role of social gaze in human-robot interaction, both how to measure social gaze behavior by humans and how to implement it in robots that interact with them. Gaze directed at an interaction partner has become a subject of increased attention in human-robot interaction research. While traditional robotics research has focused work on robot gaze solely on the identification and manipulation of objects, researchers in HRI have come to recognize that gaze is a social behavior in addition to a way of sensing the world. This workshop will approach the problem of understanding the role of social gaze in human-robot interaction from the dual perspectives of investigating human-human gaze for design principles to apply to robots and of experimentally evaluating human-robot gaze interaction in order to assess how humans engage in gaze behavior with robots.

Mutual gaze is an important aspect of face-to-face communication that arises from the interaction of the gaze behavior of two individuals. In this dual eye-tracking study, gaze data was collected from human conversational pairs with the goal of gaining insight into what characteristics of the conversation partners influence this behavior. We investigate the link between personality, familiarity and mutual gaze. The results found indicate that mutual gaze behavior depends on the characteristics of both partners rather than on either individual considered in isolation. We discuss the implications of these findings for the design of socially appropriate gaze controllers for robots that interact with people.

In this paper we describe the design of an approach to evolve Sims's creatures with morphology and behaviour similar to biped animals. Our hypothesis is that biases in morphology that encourage limb specialisation, combined with rewards for successful locomotion and carrying at the same time and realistic, physics-based penalties for falling in fitness function, would lead to creatures capable of bipedal locomotion. We present experimental results demonstrating successful evolution of biped morphology and stepping gait.

In this paper, a modelling approach is described that represents human-robot social interactions as partially observable Markov decision processes (POMDPs). In these POMDPs, the intention of the human is represented as an unobservable part of the state space, and the robot's own intentions are expressed through the rewards. The state transition structure for the models is created using action rules that capture the effects of the robot's actions, relate the human's behavior to their intentions, and describe the changing state of the environment. State transitions are modified using data from humans interacting with other humans. The policies obtained by solving these models are used to control a robot in a socially situated task with a human partner. These interactions are compared to those of human pairs performing the same task, demonstrating that this approach produces policies that exhibit natural and socially appropriate behavior.