Robot navigation plays a critical role in how people perceive, accept, and collaborate with robots in shared environments. This study presents PRoMo (Preference for Robot Motion Questionnaire), a user-centered tool designed to capture human expectations of robot navigation behavior, independent of specific robot forms or tasks. The questionnaire consolidates 28 empirically grounded behaviors into five thematic categories: safety, predictability, proximity, speed and path selection, and responsiveness. Responses from 142 participants reveal strong preferences for navigation strategies that respect personal space, avoid blind spots, and signal awareness through subtle motion cues. Open-ended responses highlight additional concerns, including robot noise and emotional comfort, suggesting that movement is experienced not only as spatial but also as sensory and expressive. Importantly, subjective familiarity with robots showed stronger correlations with behavior preferences than objective experience. These findings provide a generalizable framework for designing socially appropriate robot navigation strategies in human-centered environments. The questionnaire also serves as a practical evaluation tool to guide the development and testing of real-world robot navigation systems.

As society witnesses an increasing presence of robots in domains such as healthcare, education, and service industries, understanding user perceptions and acceptance becomes essential. This research investigates the connection between the perception of robot behavior and user experience, emphasizing the role of social characteristics in shaping perceptions. A sample of 240 participants (mean age 39) evaluated scenarios with non-anthropomorphic robots exhibiting different behavior-one scenario where the robot displayed social behavior (social sensitivity, attention-sharing, and helping) and another where it did not. Insights from the literature underscore the importance of user experience, cultural differences, and prior exposure to robots in shaping attitudes. The present paper replicates the evidence that experience with robots impacts the perception of robots. The novel finding is that users with greater experience prefer robots that show social behavior. The experiments utilized the Mind Attribution Scale, Godspeed Scale, Robotic Social Attributes Scale, and a Prior Experience with Robots Questionnaire. ANCOVA analysis revealed a significant interaction between robot behavior and participants' experience on the perception of the robots. Results indicated that as participants' experience increased, robots with social behavior received higher ratings across all instruments, affirming the impact of personal experiences on shaping perceptions. The study contributes valuable insights into the dynamics of human-robot interaction, guiding the programming of robot behavior for enhanced user experience and societal acceptance in various domains where robots are increasingly present.

In recent years, there has been a growing trend of integrating robots into various dimensions of daily life, to improve user experiences and offer a range of services. This study explores the interactions of robots showing social behavior towards other robots and their impact on human perceptions. We focus on three types of social behavior: social sensitivity, attention-sharing, and helping, aiming to understand how these interactions affect human perceptions of robots. Utilizing Duckiebot mobile robots in carefully crafted experimental setups, participants observed video recordings of these robots’ interactions, which either included or excluded each targeted social behavior. The study measured user responses using established scales such as the Mind Attribution Scale (MAS), the Goodspeed Scale, and the Robotic Social Attributes Scale (RoSAS). The results demonstrated that robots displaying social behavior towards other robots were perceived more positively compared to those that did not exhibit such behavior. Specifically, social sensitivity positively impacted animacy, experience, likability, perceived intelligence, safety, and warmth. Attention-sharing improved perceptions of competence, experience, likability, perceived intelligence, and warmth. Additionally, helping behavior positively affected agency, animacy, anthropomorphism, competence, experience, likability, perceived intelligence, safety, and warmth. This research contributes valuable insights into Human-Robot Interaction (HRI), highlighting the significant impact of robots’ interactions with each other on user experiences and perceptions. The exploration of social behavior lays a foundation for designing robots that evoke positive responses, fostering smoother integration of robotic technology into various aspects of society.

Self-disclosing to others can benefit emotional well-being, but socio-emotional barriers can limit people’s ability to do so. Self-disclosing towards social robots can help overcome these obstacles as robots lack judgment and can establish rapport. To further understand the influence of affective factors on people’s self-disclosure to social robots, this study examined the relationship between self-disclosure behaviour towards a social robot and people’s emotional states and their perception of the robot’s responses as comforting (i.e., being emphatic). The study included 1160 units of observation collected from 39 participants who conversed with the social robot Pepper (SoftBank Robotics) twice a week for 5 weeks (10 sessions in total), answering three personal questions in each session. Results show that perceiving the robot’s responses as more comforting was positively related to self-disclosure behaviour (in terms of disclosure duration in seconds, and disclosure length in number of words), and negative emotional states, such as lower mood, and higher feelings of loneliness and stress, were associated with higher rates of self-disclosure towards the robot. Additionally, higher rates of introversion significantly predicted higher rates of self-disclosure towards the robot. The study reveals the meaningful influence of affective states on how people behave when talking to social robots, especially when experiencing negative emotions. These findings may have implications for designing and developing social robots in therapeutic contexts.

Since interactions with social robots are novel and exciting for many people, one particular concern in this specific area of human-robot interaction (HRI) is the extent to which human users will experience the interactions positively over time, when the robot’s novelty is particularly salient. In the current paper, we investigated users’ experience in long-term HRIs; how users perceive the ongoing interactions and the robot’s ability to sustain it over time. Therefore, here we examine the effect of the repeated measures (10 testing sessions) and the discussion theme (Covid-19 related vs general) on the way participants experienced the interaction quality with a social robot and perceived the robot’s communication competency over time. We found that despite individual differences between the participants, over time participants found the interactions with Pepper to be of higher quality and that Pepper’s communication skills got better. Nevertheless, our results also stressed that the discussion theme has no meaningful nor significant effect on the way people perceive Pepper and the interaction.

Constructs intended to capture social attitudes and behaviour towards social robots are incredibly varied, with little overlap or consistency in how they may be related. In this study we conduct exploratory analyses between participants’ self-reported attitudes and behaviour towards a social robot. We designed an autonomous interaction where 102 participants interacted with a social robot (Pepper) in a hypothetical travel planning scenario, during which the robot displayed various multi-modal social behaviours. Several behavioural measures were embedded throughout the interaction, followed by a self-report questionnaire targeting participant’s social attitudes towards the robot (social trust, liking, rapport, competency trust, technology acceptance, mind perception, social presence, and social information processing). Several relationships were identified between participant’s behaviour and self-reported attitudes towards the robot. Implications for how to conceptualise and measure interactions with social robots are discussed.

Learning tasks designed with social or educational robots are becoming increasingly commonplace, with emerging methodologies being proposed regarding the design of interaction patterns for child-robot-interaction (cHRI). Yet, technological limitations remain a strong barrier to the implementation of fully autonomous robot tutoring systems. In addition, there is currently no research on how social and educational robots might be combined when designing learning tasks. Consequently, in this paper we describe the design and pilot testing of a (semi) autonomous learning task designed with a social robot (NAO) and educational robot (Cozmo) targeted at children’s computational thinking skills. Preliminary data from a pilot phase with 53 children is promising, and results are discussed with regards to identified challenges. Several solutions are proposed in the context of designing interactions for cHRI.

Mind perception in robots has been an understudied construct in human-robot interaction (HRI) compared to similar concepts such as anthropomorphism and the intentional stance. In a series of three experiments, we identify two factors that could potentially influence mind perception and moral concern in robots: how the robot is introduced (framing), and how the robot acts (social behaviour). In the first two online experiments, we show that both framing and behaviour independently influence participants' mind perception. However, when we combined both variables in the following real-world experiment, these effects failed to replicate. We hence identify a third factor post-hoc: the online versus real-world nature of the interactions. After analysing potential confounds, we tentatively suggest that mind perception is harder to influence in real-world experiments, as manipulations are harder to isolate compared to virtual experiments, which only provide a slice of the interaction.

Whilst there has been growing interest in the use of social robots in educational settings, the majority of this research focuses on learning outcomes, with less emphasis on the social processes surrounding these interactions. One such understudied factor is children's trust in the robot as a teacher. Trust is a relevant domain in that if and how children trust a robot could influence their subsequent learning outcomes. The extent to which the robot's behaviour (including making errors) influences trust is yet to be fully explored. Consequently, the goal of this research is to determine the role of trust in children's learning from social robots. We report a pilot study investigating the conceptualisation and measurement of chil-dren's trust in robots. 33 children aged between 4-9 completed a computational thinking learning task with a NAO robot at a Science Festival. Observations of the interactions in terms of developing tasks and measurements for child robot interaction are discussed. The findings tentatively suggest children's trust in the robot can be divided into two parts: social affiliation towards the robot, and perceived competence/reliability of the robot.

Agents (virtual/physical) in a learning environment can be introduced in different roles, such as a tutor, mentor, motivator, expert, peer student etc. Each agent type brings an expertise, creating a unique social relationship with students. Depending on their role, agents have specific goals and beliefs, as well as attitudes towards the learners, thereby influencing different aspects of learning such as cognitive, affective and meta-cognitive processes in a learner. The proposed research will primarily investigate the meta-cognitive aspect of self-regulation in collaborative learning interactions and its variations with various scaffolding strategies based on agent roles. The learning interaction will be based on the socially shared regulation model of self regulation, which accommodates the social context of self regulated learning created by agents in multiples roles and behaviours. The objectives of this research will be to understand how various roles and behaviours of the agents would influence the self regulation skills of the learner and to design a role-based strategy selection model for regulation scaffolding, based on the behavioural, motivational and cognitive measures of the learning interaction.

In this video submission, we describe a scenario developed in the EMOTE project. The overall goal of the EMOTE project is to develop an empathic robot tutor for 11-13 year old school students in an educational setting. The pedagogical domain here is to assist students in learning and testing their map-reading skills typically learned as part of the geography curriculum in schools. We show this scenario with a NAO robot interacting with the students whilst performing map-reading tasks on a touch-screen device in this video.

Human and robot tutors alike have to give careful consideration as to how feedback is delivered to students to provide a motivating yet clear learning context. Here, we performed a perception study to investigate attitudes towards negative and positive robot feedback in terms of perceived emotional valence on the dimensions of 'Pleasantness', 'Politeness' and 'Naturalness'. We find that, indeed, negative feedback is perceived as significantly less polite and pleasant. Unlike humans who have the capacity to leverage various paralinguistic cues to convey subtle variations of meaning and emotional climate, presently robots are much less expressive. However, they have one advantage that they can combine synthetic robotic sound emblems with verbal feedback. We investigate whether these sound emblems, and their position in the utterance, can be used to modify the perceived emotional valence of the robot feedback. We discuss this in the context of an adaptive robotic tutor interacting with students in a multimodal learning environment.

Engagement in task orientated social robotics is a complex phenomenon, consisting of both task and social elements. Previous work in this area tends to focus on these aspects in isolation without consideration for the positive or negative effects one might cause the other. We explore both, in an attempt to understand how engagement with the task might effect the social relationship with the robot, and vice versa. In this paper, we describe the analysis of participant self-report data collected during an exploratory pilot study used to evaluate users' “perception of engagement”. We discuss how the results of our analysis suggest that ultimately, it was the users' own perception of the robots' characteristics such as friendliness, helpfulness and attentiveness which led to sustained engagement with both the task and robot.

Computer-related anger is compared with driving-related anger in the context of considering whether the concept of ethopoeia can help in explaining computer-related anger and to test whether appraisal theory applies to human–computer interactions to the same extent as it does to interactions between humans. Using retrospective self-report questionnaires, a pool of 140 UK students and members of the public provided data on recent incidents in which they experienced anger while using a computer and while driving. The motivational relevance of incidents and need to communicate anger to computers are shown to be independently predictive of computer anger intensity. Also, as a group, all three appraisal components (motivational relevance, motivational incongruence and other-accountability) that are taken to be central in the generation of anger in the Smith and Lazarus variant of appraisal theory are shown to be more predictive of anger intensity in computing situations than in driving situations. Findings such as computers and other drivers being held equally accountable for anger-inducing incidents, and computer-accountability and other-driver-accountability being equally correlated with anger intensity across the two situations are argued to be consistent with the idea that ethopoeia may play a role in many instances of computer-related anger.

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-of-the-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.

Out-group members are commonly viewed as being less human than in-group members. They are denied certain human characteristics and in turn become associated with machines or automata. Specifically, out-groups are attributed less naturally and uniquely human traits, and they are also seen as being less able to experience complex emotions in comparison to the in-group. Such dissociations have been demonstrated with real human faces but in our study, we aimed to test whether similar effects generalize to their artificial versions. Caucasian participants were presented with images of male Caucasian and Indian faces. Their task was to evaluate to what extent naturally and uniquely human traits, as well as primary and secondary emotions, can be attributed to them. In line with previous research, it was found that positive naturally human traits were attributed to a greater degree to the in-group than to the out-group, applying to both real and artificial faces. Moreover, negative naturally human traits and negative primary emotions were attributed more to the out-group. This indicates a positive bias towards the in-group and subtle out-group derogation. The results extend prior research based on real human faces and show that intergroup processes emerge similarly in response to artificial faces, which may have implications for the fields of computer graphics and animation. That is, even the most realistic face recognized as belonging to an out-group member may convey less humanness than that of an in-group member.

Most sentiment analysis approaches deal with binary or ordinal prediction of affective states (e.g., positive versus negative) on review-related content from the perspective of the author. The present work focuses on predicting the emotional responses of online communication in nonreview social media on a real-valued scale on the two affective dimensions of valence and arousal. For this, a new dataset is introduced, together with a detailed description of the process that was followed to create it. Important phenomena such as correlations between different affective dimensions and intercoder agreement are thoroughly discussed and analyzed. Various methodologies for automatically predicting those states are also presented and evaluated. The results show that the prediction of intricate emotional states is possible, obtaining at best a correlation of 0.89 for valence and 0.42 for arousal with the human assigned assessments.

This paper presents an integrated view on a series of experiments conducted with an affective dialog system, applied as a tool in studies of emotions and social processes in online communication. The different realizations of the system are evaluated in three experimental setups to verify effects of affective profiles, as well as of fine-grained communication scenarios on users' expressions of affective states, experienced emotional changes, and interaction patterns. Results demonstrate that the system applied in virtual reality settings matches a Wizard-of-Oz in terms of chatting enjoyment, dialog coherence, and realism. Variants of the system's affective profile significantly influence the rating of chatting enjoyment and an emotional connection. Self-reported emotional changes experienced by participants during an interaction with the system are in line with the type of applied profile. Analysis of interaction patterns, i.e., usage of particular dialog act classes, word categories, and textual expressions of affective states for different scenarios, demonstrates that a communication scenario for social sharing of emotions was successfully established. The experimental evidence provides valuable input for applications of affective dialog systems and strengthens them as valid tools for studying affect and social aspects in online communication.

Abstract A huge number of informal messages are posted every day in social network sites, blogs, and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behavior to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially oriented, designed to identify opinions about products rather than user behaviors. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimized by machine learning, SentiStrength is able to predict positive emotion with 60.6% accuracy and negative emotion with 72.8% accuracy, both based upon strength scales of 1–5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.

The survey by Calvo and D'Mello presents a useful overview of the progress of and issues in affect detection. They focus on emotion theories that are relevant to Affective Computing (AC) and suggest stronger collaborations between disciplines. My contribution emphasizes the importance of these issues for AC. In fact, empirical research strongly suggests that facial, vocal, and bodily expressions, subjective experience, and physiological changes are often not very highly correlated in spontaneous situations. Overestimating this cohesion limits the usefulness of affect detection methods in real-world applications. Other factors, such as social context, knowledge regarding the goals of certain interactions, as well as interindividual differences are critically important factors for improving affect detection. At times, social concepts, such as politeness, might be more conducive to model realistic behavior. Knowledge on affect perception is important to estimate the level of realism required to create satisfying and productive interactions between users and artificial systems. Interdisciplinary joint research between social and biological scientists on the one hand and computer scientists and engineers on the other is necessary to deal with the complexity of affective processes. All disciplines involved have much to gain in the process.