This study investigates whether the opinions of robotic agents can influence human decision-making when robots display value awareness (i.e., the capability of understanding human preferences and prioritizing them in decision-making). We designed an experiment in which participants interacted with two Furhat robots - one programmed to be Value-Aware and the other Non-Value-Aware - during a labeling task for images representing human values. Results indicate that participants distinguished the Value-Aware robot from the Non-Value-Aware one. Although their explicit choices did not indicate a clear preference for one robot over the other, participants directed their gaze more toward the Value-Aware robot. Additionally, the Value-Aware robot was perceived as more loyal, suggesting that value awareness in a social robot may enhance its perceived commitment to the group. Finally, when both robots disagreed with the participant, conformity occurred in about one out of four trials, and participants took longer to confirm their responses, suggesting that two robots expressing dissent may introduce hesitation in decision-making. On one hand, this highlights the potential risk that robots, if misused, could manipulate users for unethical purposes. On the other hand, it reinforces the idea that social robots could encourage reflection in ambiguous situations and help users avoid scams.

Expectations critically shape how people form judgments about robots, influencing whether they view failures as minor technical glitches or deal-breaking flaws. This work explores how high and low expectations, induced through brief video priming, affect user perceptions of robot failures and the utility of explanations in HRI. We conducted two online studies ( N = 600 total participants); each replicated two robots with different embodiments, Furhat and Pepper. In our first study, grounded in expectation theory, participants were divided into two groups, one primed with positive and the other with negative expectations regarding the robot's performance, establishing distinct expectation frameworks. This validation study aimed to verify whether the videos could reliably establish low and high-expectation profiles. In the second study, participants were primed using the validated videos and then viewed a new scenario in which the robot failed at a task. Half viewed a version where the robot explained its failure, while the other half received no explanation. We found that explanations significantly improved user perceptions of Furhat, especially when participants were primed to have lower expectations. Explanations boosted satisfaction and enhanced the robot's perceived expressiveness, indicating that effectively communicat-Authors contributed equally. By contrast, Pepper's explanations produced minimal impact on user attitudes, suggesting that a robot's embodiment and style of interaction could determine whether explanations can successfully offset negative impressions. Together, these findings underscore the need to consider users' expectations when tailoring explanation strategies in HRI. When expectations are initially low, a cogent explanation can make the difference between dismissing a failure and appreciating the robot's transparency and effort to communicate. Keywords: Expectations, Explanations, Explainability, Human-Robot Interaction, Priming 1. Introduction When robots operate in human environments, user expectations play a crucial role in shaping human-robot interaction (HRI) (Lohse, 2009; Horstmann and Kr amer, 2020; Dogan et al., 2025). However, there is often a mismatch between these expectations and the actual capabilities of social robots (Ros en et al., 2022), which can lead to disappointment and, consequently, diminish the quality of interactions (Olson et al., 1996; Kruglanski and Sleeth-Keppler, 2007). For instance, a user might expect robots to function as proactive and autonomous assistants, yet when robots make mistakes due to their limited abilities, this mismatch can undermine the robot's perceived trustworthiness and competence (Salem et al., 2015; Cha et al., 2015).

This paper presents the implementation and evaluation of a conversational agent designed for multi-party open-ended interactions. Leveraging state-of-the-art technologies such as voice direction of arrival, voice recognition, face tracking, and large language models, the system aims to facilitate natural and intuitive human-robot conversations. Deployed on the Furhat robot, the system was tested with 30 participants engaging in open-ended group conversations and then in two overlapping discussions. Quantitative metrics, such as latencies and recognition accuracy, along with qualitative measures from user questionnaires, were collected to assess performance. The results highlight the system's effectiveness in managing multi-party interactions, though improvements are needed in response relevance and latency. This study contributes valuable insights for advancing human-robot interaction, particularly in enhancing the naturalness and engagement in group conversations.

Addressing the critical shortage of mental health resources for effective screening, diagnosis, and treatment remains a significant challenge. This scarcity underscores the need for innovative solutions, particularly in enhancing the accessibility and efficacy of therapeutic support. Embodied agents with advanced interactive capabilities emerge as a promising and cost-effective supplement to traditional caregiving methods. Crucial to these agents' effectiveness is their ability to simulate non-verbal behaviors, like backchannels, that are pivotal in establishing rapport and understanding in therapeutic contexts but remain under-explored. To improve the rapport-building capabilities of embodied agents we annotated backchannel smiles in videos of intimate face-to-face conversations over topics such as mental health, illness, and relationships. We hypothesized that both speaker and listener behaviors affect the duration and intensity of backchannel smiles. Using cues from speech prosody and language along with the demographics of the speaker and listener, we found them to contain significant predictors of the intensity of backchannel smiles. Based on our findings, we introduce backchannel smile production in embodied agents as a generation problem. Our attention-based generative model suggests that listener information offers performance improvements over the baseline speaker-centric generation approach. Conditioned generation using the significant predictors of smile intensity provides statistically significant improvements in empirical measures of generation quality. Our user study by transferring generated smiles to an embodied agent suggests that agent with backchannel smiles is perceived to be more human-like and is an attractive alternative for non-personal conversations over agent without backchannel smiles.

Gaze cues play an important role in human communication and are used to coordinate turn-taking and joint attention, as well as to regulate intimacy. In order to have fluent conversations with people, social robots need to exhibit human-like gaze behavior. Previous Gaze Control Systems (GCS) in HRI have automated robot gaze using data-driven or heuristic approaches. However, these systems tend to be mainly reactive in nature. Planning the robot gaze ahead of time could help in achieving more realistic gaze behavior and better eye-head coordination. In this paper, we propose and implement a novel planning-based GCS. We evaluate our system in a comparative within-subjects user study (N=26) between a reactive system and our proposed system. The results show that the users preferred the proposed system and that it was significantly more interpretable and better at regulating intimacy.

That starts with recruitment and onboarding. "Having a face-to-face meeting with a human seems to be an incredibly powerful way to communicate," Samer Al Moubayed, co-founder and CEO of Furhat Robotics, told Engadget. However, he points out that even the most experienced and well-trained recruiters occasionally succumb to subconscious biases while conducting interviews -- be they based on age, gender, race or even just a candidate's responses to pre-interview chit-chat. And that's where Furhat's social robot comes in. The 16-inch tall, nearly 8-pound robot is designed to sit at eye level and provide a physical presence with which to interact, as opposed to an onscreen chatbot or virtual phone assistant.

According to a recent TNG survey, 73 percent of job seekers in Sweden believe they've been discriminated against during the job application process. By replacing the human recruiter with Tengai, TNG and Furhat believe they can make the screening process more fair while still providing a "human" touch. "I was quite sceptical at first before meeting Tengai, but after the meeting I was absolutely struck," healthcare recruiter Petra Elisson, who has been involved in the testing, told the BBC. "At first I really, really felt it was a robot, but when going more deeply into the interview I totally forgot that she's not human." As for ensuring that Tengai doesn't reflect the biases of its creators and training data -- a problem that has cropped up with other AIs -- Furhat's chief scientist, Gabriel Skantze, told the BBC the company is making it a point to conduct test interviews with a diverse mix of recruiters and volunteers before Tengai is ever in the position to actually decide an applicant's employment fate.

Subconscious bias on the part of potential employers could become a thing of the past, thanks to a'robo-interviewer' currently in development. Tengai, a torso-less robot that speaks and smiles, will judge you purely on your abilities - leaving race, gender and other potentially influencing factors aside. Sweden's largest recruitment company TNG is already using the robot with the human-like interface in a series of trials. Tengai (pictured), the torso-less robot that speaks and smiles, is being trained at Sweden's largest recruitment company TNG to learn to conduct interviews through AI technology. Furhat is a social robot created by Stockholm-based startup Furhat Robotics.

Eerie lifelike social robot Furhat exudes empathy and warmth, encouraging people to open up more than they do to friends, its creator claims. The robot, a three-dimensional bust with a projection of a human-like face, aims to build on our new-found ease talking to voice assistants like Siri and Alexa. Furhat does this by persuading people to interact with it as if it were a person, picking up on our cues to strike up a rapport. Yet precisely because it isn't human, and is therefore free from bias, the robot can spur people to engage more honestly, its creator says, making it useful in situations such as screening for health risks where people often lie. Furhat is a social robot created by Stockholm-based startup Furhat Robotics.

LONDON – Furhat tilts his or her head, smiles, exudes empathy and warmth, and encourages us to open up. The robot, a 3D bust with a projection of a humanlike face, aims to build on our newfound ease talking to voice assistants like Siri and Alexa, by persuading us to interact with it as if it were a person, picking up on our cues to strike up a rapport. Yet precisely because it isn't human, and is therefore free from bias, the robot can spur people to engage more honestly, its creator says, making it useful in situations such as screening for health risks where people often lie. "We've seen research that shows that in certain situations people are more comfortable opening up and talking about difficult issues with a robot than with a human," said Samer Al Moubayed, chief executive of Furhat Robotics. That's because a robot's personality can mirror the personality of the person interacting with it and because people don't feel judged, he added.