Affective robotics centers on recognizing emotional states and generating artificial emotions through embodied robotic systems. This paper surveys the current state of the field, with a particular focus on bodily expressed emotion—both in recognizing affect through body movements and postures, and in generating movement that is parsed as affect by human observers. Framed through the lens of the performing arts, this examination provides insights into the expressive potential of robots, motivates key open questions, and highlights challenging problems, as presented through an art-inspired case study and foundational background material. A close engagement with the performing arts suggests intense malleability and diversity of bodily expression, challenging some of the field's prevailing goals—such as designing generally “happy” robotic movement—and emphasizing the importance of variables such as context and interactional intent. The paper concludes by proposing future directions for bodily expressed affective robotics that integrate advances from both robotics and the performing arts.

As robots are becoming increasingly common within social settings, understanding the drivers of effective Human-Robot Interaction (HRI) becomes crucial. One factor emerging as a potential key variable in HRI is social presence, which is the extent to which a person feels connected to, or aware of, the presence of another. In the context of HRI, social presence has been identified as a factor that shapes human responses to robots, impacting outcomes such as attachment, trust, and social influence. Social presence itself appears to be influenced by factors including the nature of the embodiment. However, HRI research examining social presence is limited by the lack of consistent operationalization of social presence and conflation with the variance in embodiment of the robot. The current pilot study is designed to tease apart embodiment and social presence by exploring the relative impacts of interactions with two matched representations of a social robot (physical and virtual), finding that even controlling for size, proximity and response mechanisms, physical embodiment is associated with higher reported social presence. The study also identified both consistencies and inconsistencies in the measurement of social presence across assessments. The findings provide a foundation for further study into social presence in HRI, by clarifying mechanisms of quantifying and experimentally manipulating social presence, allowing insight into ways in which this factor drives HRI.

Social Presence (SP) is a critical factor in human-robot interaction (HRI), influencing how users perceive and engage with social robots. Traditionally, SP has been conceptualized by focusing on the user's subjective perception of a robot's social capabilities, which we label, Attributed Social Presence (ASP). However, this perspective overlooks the inherent capabilities of robots that can engender a sense of presence, which we argue can be termed, Intrinsic Social Presence (ISP). In this paper we explicitly explore and distinguish between ASP and ISP, highlighting the limitations of solely focusing on attributed user perceptions in HRI. We address the challenges associated with the conceptual ambiguity of SP, the difficulties in benchmarking due to inconsistent definitions, and the lack of research examining robot presence modulation. To overcome these issues, we propose reframing SP as a multidimensional construct encompassing three interconnected domains. By emphasizing ISP and providing a practical framework for its implementation, this paper aims to enhance the development of adaptable, responsive robots capable of meaningful engagement. This approach bridges the gap between rigid robotic behaviour and the fluid, adaptive presence characteristic of human interactions, emphasizing that genuine trust and connection are integral to the full functionality and effectiveness of social robots.

Attention serves as a critical antecedent to social presence, which fundamentally influences acceptance, trust, and overall interaction quality in human-robot interaction (HRI). This paper investigates the development of a gaze modulation framework that enables robots to strategically influence human attention through two complementary Q-learning-based modules: Gaze-Garnering Modulation (GGM) and Gaze-Avoidance Modulation (GAM). To measure gaze feedback, we introduce a novel metric—the Dynamic Gaze Engagement Index (DGEI)—that integrates attention ratio with stationary gaze entropy (SGE) to evaluate not just the quantity but also the quality of visual attention. This feedback allows the system to continuously adapt to each individual’s unique attentional patterns and thresholds, providing personalised interaction. In two experiments, 20 participants interacted with a Pepper robot that dynamically adjusted its behaviours (lights, movements, and voice volume) based on real-time gaze feedback. Results demonstrated that GGM significantly enhanced gaze engagement, fostering strong mutual interaction, while GAM effectively redirected attention when appropriate, with participants reporting lower perceived gaze engagement in this condition. Post-experiment questionnaires using the "Psycho-behavioural Interaction - Perceived Attentional Engagement" section of the Networked Minds Social Presence Inventory (NMSPI) revealed significant differences between conditions (t(18)=2.47, p=0.0238), validating the attention modulation by each module and corroborating the behavioural observations. These findings underscore the importance of adaptive robotic behaviours in facilitating dynamic and unobtrusive interactions.

The aged care industry is under pressure from stressors including increasing resident numbers and difficulty meeting staffing requirements. Robots may be able to support the industry by filling many vital roles, however it is currently unclear how successful implementation of robots in aged care can occur, and detailed in situ assessment and mapping of robotic deployment in these settings is lacking. The current case study examines early-stage implementation of robots at an aged care home in Australia, assessing logistical, technical and person factors. Key facilitators and barriers to successful deployment are identified, including identifying needs and roles, health and safety issues and technical support. The findings illustrate the potential for robots in aged care and provide a blueprint for the steps needed for long-term effectiveness and commercial viability.

The face is the most influential feature in any interaction. This study investigated the effect of robot faces in embodied interactions on a range of subjective and objective factors. The platform used to answer the research question was an interactive robotic art installation, incorporating a robot arm that could be presented with or without a face displayed on an attached screen. Participants were exposed to one of three conditions – the robot arm only, the robot arm with a static face displayed, and the robot arm with a dynamic face displayed. We used the Godspeed Questionnaire to determine whether the different embodiments would be perceived differently on measures of likeability, animacy, and safety before and after the interaction. We also measured how close participants stood to the robot and how much time they spent interacting with the robot. We found that the three embodiments did not significantly differ in time spent, distance, animacy, likeability, or safety before and after the interaction. This surprising result hints at other possible reasons that influence the success of a robot interaction and advances the understanding of the effect of faces in human-robot interaction.

A plethora of commercial social robots and social robotics startups have risen over the last few years. At a cursory glance, most such robots are merely conversational agents, essentially offering a similar or subset of the capabilities of a smart communication device embodied in a mobile/semi-mobile robotic platform. This raises the question of the efficacy of such an approach. In this paper, we explore embodiment using a social, in-the-wild interaction scenario, comparing a Human-Computer and Human-Robot context. A public site has been deliberately chosen to highlight the importance of conducting such user studies in unconstrained social settings as opposed to in controlled lab settings. Increasing evidence suggest the lack of generalizability of lab-based results in the wild, which we argue as a reason for misguided commercialization of social robots and their eventual commercial failures. The results have implications for the longterm commercial viability of such social robots.