Brian Scassellati

Introduction: While shared activities foster connection between people living with dementia (PLWD) and their care partners, emotional distress and daily caregiving responsibilities often make them difficult to initiate. This paper investigates the adaptation of a socially assistive robot, Ommie, to guide shared deep breathing and singing activities for these pairs. Methods: We refined the robot's behaviors through two interaction design sessions with people living with dementia and care partners, mediated by an occupational therapist. In a subsequent study with 17 pairs, participants engaged in deep breathing and singing activities with the robot as well as in-session semi-structured interviews, and we conducted post-hoc video analysis to explore their interactional dynamics. Results: Participants reported the interactions as easy to follow, calming, and familiar. Post-hoc video analysis revealed patterns of intimacy and synchrony, including frequent physical touch, mutual gaze, and rhythmic coordination. We also observed instances of personal memory recall and a playful atmosphere, in which pairs often used humor as a coping mechanism after deviations from the robot's instructions. Discussion: From our observations, we discuss three design opportunity spaces: the robot as the focus for synchronization, as an instrument of joint play, and as a source of familiarity versus variety.

We present the Ommie Deployable System (DS), a replicable, autonomous platform for long-term, in-the-wild mental health applications with the Ommie robot. Ommie DS builds on prior anxiety-focused deployments by introducing robust hardware, enhanced sensing, modular software, a companion tablet, and wireless multi-device architecture to support daily deep-breathing interactions in homes. Designed using off-the-shelf components and rapid-prototyped enclosures, the system enables reliable multi-week use, remote monitoring, and easy customization. By providing a durable, open, and researcher-friendly platform, Ommie DS supports scalable, real-world study of HRI for mental health and well-being.

To collaborate with humans, robots must infer goals that are often ambiguous, difficult to articulate, or not drawn from a fixed set. Prior approaches restrict inference to a predefined goal set, rely only on observed actions, or depend exclusively on explicit instructions, making them brittle in real-world interactions. We present BALI (Bidirectional Action–Language Inference) for goal prediction, a method that integrates natural language preferences with observed human actions in a receding-horizon planning tree. BALI combines language and action cues from the human, asks clarifying questions only when the expected information gain from the answer outweighs the cost of interruption, and selects supportive actions that align with inferred goals. We evaluate the approach in collaborative cooking tasks, where goals may be novel to the robot and unbounded. Compared to baselines, BALI yields more stable goal predictions and significantly fewer mistakes.

The fields of human-robot interaction (HRI) and robotics at large have developed around a stable set of assumptions about what robots are and how they should behave. These assumptions arise from the constitutive traits of robots, which together shape social expectations. Over time, these expectations have hardened into tacit rules that quietly govern research and design: robots should always engage, help, be productive, remain polite, never lie, never err, and never model harm. While these prevailing norms have merit, they also constrain the field's imagination of the interactions robots can meaningfully support. We propose rule-breaking as a generative design strategy and illustrate how deliberate violations—robots that interrupt, refuse, mislead, or err—can produce interactions that are more ethical, effective, and socially intelligent. In doing so, we argue for a more reflexive and imaginative HRI that learns as much from breaking the rules as from following them.

Human-Robot Interaction (HRI) continues to rely on commercial social robot platforms to support academic research. Yet again and again, these systems prove short-lived, inaccessible, or misaligned with research needs. We argue that this is not an industry problem – the goals, needs, and constraints of industry are inherently distinct. Instead, this is a fundamental structural problem in HRI research, and one that must be solved from within. In short, HRI researchers must build their own products. In this paper, we trace the recent problems of industry-supplied robots and frame a new type of HRI research artifact in response: Deployable Research Products (DRPs), which bridge the gap between lab prototypes and commercial products. Drawing on mental models from business and innovation theory, we outline the mindset shifts that HRI must embody to move towards DRPs. We conclude with three emerging examples of this alternative path in the HRI community. These projects differ in scope and approach but share a common thread: to ensure the longevity of our science, we cannot outsource what we value most.

In this work, we introduce and formalize the Zero-Knowledge Task Planning (ZKTP) problem, i.e., formulating a sequence of actions to achieve some goal without task-specific knowledge. Additionally, we present a first investigation and approach for ZKTP that leverages a large language model (LLM) to decompose natural language instructions into subtasks and generate behavior trees (BTs) for execution. If errors arise during task execution, the approach also uses an LLM to adjust the BTs on-the-fly in a refinement loop. Experimental validation in the AI2-THOR simulator demonstrate our approach's effectiveness in improving overall task performance compared to alternative approaches that leverage task-specific knowledge. Our work demonstrates the potential of LLMs to effectively address several aspects of the ZKTP problem, providing a robust framework for automated behavior generation with no task-specific setup.

In this work, we introduce and formalize the Zero-Knowledge Task Planning (ZKTP) problem, i.e., formulating a sequence of actions to achieve some goal without task-specific knowledge. Additionally, we present a first investigation and approach for ZKTP that leverages a large language model (LLM) to decompose natural language instructions into subtasks and generate behavior trees (BTs) for execution. If errors arise during task execution, the approach also uses an LLM to adjust the BTs on-the-fly in a refinement loop. Experimental validation in the AI2-THOR simulator demonstrate our approach's effectiveness in improving overall task performance compared to alternative approaches that leverage task-specific knowledge. Our work demonstrates the potential of LLMs to effectively address several aspects of the ZKTP problem, providing a robust framework for automated behavior generation with no task-specific setup.

For effective human-robot collaboration, a robot must align its actions with human goals, even as they change mid-task. Prior approaches often assume fixed goals, reducing goal prediction to a one-time inference. However, in real-world scenarios, humans frequently shift goals, making it challenging for robots to adapt without explicit communication. We propose a method for detecting goal changes by tracking multiple candidate action sequences and verifying their plausibility against a policy bank. Upon detecting a change, the robot refines its belief in relevant past actions and constructs Receding Horizon Planning (RHP) trees to actively select actions that assist the human while encouraging Differentiating Actions to reveal their updated goal. We evaluate our approach in a collaborative cooking environment with up to 30 unique recipes and compare it to three comparable human goal prediction algorithms. Our method outperforms all baselines, quickly converging to the correct goal after a switch, reducing task completion time and improving collaboration efficiency.

For effective human-robot collaboration, a robot must align its actions with human goals, even as they change mid-task. Prior approaches often assume fixed goals, reducing goal prediction to a one-time inference. However, in real-world scenarios, humans frequently shift goals, making it challenging for robots to adapt without explicit communication. We propose a method for detecting goal changes by tracking multiple candidate action sequences and verifying their plausibility against a policy bank. Upon detecting a change, the robot refines its belief in relevant past actions and constructs Receding Horizon Planning (RHP) trees to actively select actions that assist the human while encouraging Differentiating Actions to reveal their updated goal. We evaluate our approach in a collaborative cooking environment with up to 30 unique recipes and compare it to three comparable human goal prediction algorithms. Our method outperforms all baselines, quickly converging to the correct goal after a switch, reducing task completion time, and improving collaboration efficiency.

In this work, we introduce and formalize the Zero-Knowledge Task Planning (ZKTP) problem, i.e., formulating a sequence of actions to achieve some goal without task-specific knowledge. Additionally, we present a first investigation and approach for ZKTP that leverages a large language model (LLM) to decompose natural language instructions into subtasks and generate behavior trees (BTs) for execution. If errors arise during task execution, the approach also uses an LLM to adjust the BTs on-the-fly in a refinement loop. Experimental validation in the AI2-THOR simulator demonstrate our approach’s effectiveness in improving overall task performance compared to alternative approaches that leverage task-specific knowledge. Our work demonstrates the potential of LLMs to effectively address several aspects of the ZKTP problem, providing a robust framework for automated behavior generation with no task-specific setup.

Robots are increasingly present in our everyday environments, offering services and products. But can they influence our choices through movement alone? This paper investigates whether a robot manipulator can nudge user decisions using only its arm movements, without speech, facial expressions, or physical contact. We first identified plausible nudging motions through a bodystorming session, then designed and implemented three composite nudges (positive, neutral, and negative) using a UR5 robot arm. In a video-based online study (N=35), participants more often chose positively nudged items and avoided negatively nudged ones. A small in-person study (N=9) confirmed the effect. These results demonstrate that movement-only nudges can influence decision-making and highlight the potential of subtle physical behaviors for nonverbal persuasion.

Many schools have built de-escalation and sensory rooms to support students who experience heightened emotional states, sensory overload, or difficulty self-regulating in traditional classroom settings. Yet, effective implementation remains challenging due to diverse student needs and resource constraints. Hence, we developed RESET (Robot-Enhanced Social-Emotional Therapy), a robot for facilitating students’ self-regulation in their school’s existing de-escalation space. We present our co-design process, iterative development, and final system components. Following a fully autonomous, month-long deployment in an elementary school, we assessed the robot’s usability and impacts. Results indicate RESET integrated well into the school environment, promoting more efficient deescalation, smoother transitions back to classroom learning, and lasting impacts beyond its deployment period.

From dating to job interviews, making new friends or simply chatting with the cashier at checkout, engaging in small talk is a vital, everyday social skill.For adults with Autism Spectrum Disorder (ASD), small talk can be particularly challenging, yet it is essential for social integration, building relationships, and accessing professional opportunities.In this study, we present our development and evaluation of an in-home autonomous robot system that allows users to practice small talk.Results from the week-long study show that adults with ASD enjoyed the training, made notable progress in initiating conversations and improving eye contact, and viewed the system as a valuable tool for enhancing their conversational skills.

In the past two decades, the field of social robotics has undergone significant growth, witnessing a surge in long-term human–robot interaction (HRI) studies. This review paper provides an in-depth analysis of 120 long-term HRI studies conducted between 2003 and 2023, spanning 7 major domains including education, entertainment, and physical and mental health. We define “long-term” as studies deploying social robots with the same users for more than three sessions across 3 consecutive days, aiming to employ a comprehensive approach and identify trends in this dynamic field. Our analysis explores various aspects of these studies, from participant demographics to the characteristics of the HRI and engagement measures. The findings reveal promising trends, such as diverse age group representation, a strong focus on real-world contexts, and autonomous robot operation. We also identify gaps, notably the limited representation of studies involving teenagers and those studying workplace settings. By presenting this overview, we aim to empower the HRI community to address challenges, refine methodologies, and foster innovation in the domain of long-term HRI.

The rapid and nearly pervasive impact of artificial intelligence on fields as diverse as medicine, law, banking, and the arts has made many students who would never enroll in a computer science class become interested in understanding elements of artificial intelligence. Fueled by questions about how this technology would change their own fields, these students are not seeking to become experts in building AI systems but instead are searching for a sufficient understanding to be safe, effective, and informed users. In this paper, we describe a first-of-its-kind course offering, "Artificial Intelligence for Future Presidents" designed and taught during the spring of 2024. We share rationale on the design and structure of the course, consider how best to convey complex technical information to students without the background in programming or mathematics, and consider methods for supporting an understanding of the limits of this technology.

As robot deployments become more commonplace, people are likely to take on the role of supervising robots (i.e., correcting their mistakes) rather than directly teaching them. Prior works on Learning from Corrections (LfC) have relied on three key assumptions to interpret human feedback: (1) people correct the robot only when there is significant task objective divergence; (2) people can accurately predict if a correction is necessary; and (3) people trade off precision and physical effort when giving corrections. In this work, we study how two key factors (robot competency and motion legibility) affect how people provide correction feedback and their implications on these existing assumptions. We conduct a user study <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(N=60)$</tex> under an LfC setting where participants supervise and correct a robot performing pick-and-place tasks. We find that people are more sensitive to suboptimal behavior by a highly competent robot compared to an incompetent robot when the motions are legible <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(p=0.0015)$</tex> and predictable <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(p=0.0055)$</tex>. In addition, people also tend to withhold necessary corrections <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(p &lt; 0.0001)$</tex> when supervising an incompetent robot and are more prone to offering unnecessary ones <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(p=0.0171)$</tex> when supervising a highly competent robot. We also find that physical effort positively correlates with correction precision, providing empirical evidence to support this common assumption. We also find that this correlation is significantly weaker for an incompetent robot with legible motions than an incompetent robot with predictable motions <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(p=0.0075)$</tex>. Our findings offer insights for accounting for competency and legibility when designing robot interaction behaviors and learning task objectives from corrections.

Atypical gaze behavior is a diagnostic hallmark of Autism Spectrum Disorder (ASD), playing a substantial role in the social and communicative challenges that individuals with ASD face. This study explores the impacts of a month-long, in-home intervention designed to promote triadic interactions between a social robot, a child with ASD, and their caregiver. Our results indicate that the intervention successfully promoted appropriate gaze behavior, encouraging children with ASD to follow the robot's gaze, resulting in more frequent and prolonged instances of spontaneous eye contact and joint attention with their caregivers. Additionally, we observed specific timelines for behavioral variability and novelty effects among users. Furthermore, diagnostic measures for ASD emerged as strong predictors of gaze patterns for both caregivers and children. These results deepen our understanding of ASD gaze patterns and highlight the potential for clinical relevance of robot-assisted interventions.

Mistakes, failures, and transgressions committed by a robot are inevitable as robots become more involved in our society. When a wrong behavior occurs, it is important to understand what factors might affect how the robot is perceived by people. In this paper, we investigated how the type of transgressor (human or robot) and type of backstory depicting the transgressor's mental capabilities (default, physio-emotional, socio-emotional, or cognitive) shaped participants' perceptions of the transgressor's morality. We performed an online, between-subjects study in which participants (N =720) were first intro-duced to the transgressor and its backstory, and then viewed a video of a real-life robot or human pushing down a human. Although participants attributed similarly high intent to both the robot and the human, the human was generally perceived to have higher morality than the robot. However, the backstory that was told about the transgressors' capabilities affected their perceived morality. We found that robots with emotional backstories (i.e., physio-emotional or socio-emotional) had higher perceived moral knowledge, emotional knowledge, and desire than other robots. We also found that humans with cognitive backstories were perceived with less emotional and moral knowledge than other humans. Our findings have consequences for robot ethics and robot design for HRI.

As robot deployments become more commonplace, people are likely to take on the role of supervising robots (i.e., correcting their mistakes) rather than directly teaching them. Prior works on Learning from Corrections (LfC) have relied on three key assumptions to interpret human feedback: (1) people correct the robot only when there is significant task objective divergence; (2) people can accurately predict if a correction is necessary; and (3) people trade off precision and physical effort when giving corrections. In this work, we study how two key factors (robot competency and motion legibility) affect how people provide correction feedback and their implications on these existing assumptions. We conduct a user study ($N=60$) under an LfC setting where participants supervise and correct a robot performing pick-and-place tasks. We find that people are more sensitive to suboptimal behavior by a highly competent robot compared to an incompetent robot when the motions are legible ($p=0.0015$) and predictable ($p=0.0055$). In addition, people also tend to withhold necessary corrections ($p &lt; 0.0001$) when supervising an incompetent robot and are more prone to offering unnecessary ones ($p = 0.0171$) when supervising a highly competent robot. We also find that physical effort positively correlates with correction precision, providing empirical evidence to support this common assumption. We also find that this correlation is significantly weaker for an incompetent robot with legible motions than an incompetent robot with predictable motions ($p = 0.0075$). Our findings offer insights for accounting for competency and legibility when designing robot interaction behaviors and learning task objectives from corrections.

Atypical gaze behavior is a diagnostic hallmark of Autism Spectrum Disorder (ASD), playing a substantial role in the social and communicative challenges that individuals with ASD face. This study explores the impacts of a month-long, in-home intervention designed to promote triadic interactions between a social robot, a child with ASD, and their caregiver. Our results indicate that the intervention successfully promoted appropriate gaze behavior, encouraging children with ASD to follow the robot's gaze, resulting in more frequent and prolonged instances of spontaneous eye contact and joint attention with their caregivers. Additionally, we observed specific timelines for behavioral variability and novelty effects among users. Furthermore, diagnostic measures for ASD emerged as strong predictors of gaze patterns for both caregivers and children. These results deepen our understanding of ASD gaze patterns and highlight the potential for clinical relevance of robot-assisted interventions.

Beyond mere formality, small talk plays a pivotal role in social dynamics, serving as a verbal handshake for building rapport and understanding. For conversational AI and social robots, the ability to engage in small talk enhances their perceived sociability, leading to more comfortable and natural user interactions. In this study, we evaluate the capacity of current Large Language Models (LLMs) to drive the small talk of a social robot and identify key areas for improvement. We introduce a novel method that autonomously generates feedback and ensures LLM-generated responses align with small talk conventions. Through several evaluations -- involving chatbot interactions and human-robot interactions -- we demonstrate the system's effectiveness in guiding LLM-generated responses toward realistic, human-like, and natural small-talk exchanges.

As foundation models increasingly permeate sensitive domains such as healthcare, finance, and mental health, ensuring their behavior meets desired outcomes and social expectations becomes critical. Given the complexities of these high-dimensional models, traditional techniques for constraining agent behavior, which typically rely on low-dimensional, discrete state and action spaces, cannot be directly applied. Drawing inspiration from robotic action selection techniques, we propose the grounded observer framework for constraining foundation model behavior that offers both behavioral guarantees and real-time variability. This method leverages real-time assessment of low-level behavioral characteristics to dynamically adjust model actions and provide contextual feedback. To demonstrate this, we develop a system capable of sustaining contextually appropriate, casual conversations ("small talk"), which we then apply to a robot for novel, unscripted interactions with humans. Finally, we discuss potential applications of the framework for other social contexts and areas for further research.

This article discusses the design, development, and evaluation of Ommie , a novel socially assistive robot that supports deep breathing practices for the purposes of anxiety reduction. Research has shown that practicing deep breathing (breathing while extending one’s inhales, holds, and exhales) has a strong capacity to calm the autonomic nervous system and reduce anxiety. The robot’s primary function is to guide users through a series of deep breaths by way of haptic interactions and audio cues. We utilized a user-centered design approach and present our design methodology in addition to core decisions across robot morphology, tactility, and interactivity. As reported in prior work, the final robot prototype was tested with a two-cohort usability study (n = 43) at a local university wellness center, including participants with anxiety and those with varying levels of experience with deep breathing. Interacting with Ommie resulted in a significant reduction in STAI-6 anxiety measures across all participants, who also found the robot intuitive, approachable, and engaging. Participants also reported feelings of focus and companionship when using the robot, often elicited by the haptic interaction. This article describes how our design process and design goals contributed to these results showing Ommie’s capacity for supporting those with anxiety. Our work also serves as an example of how researchers can design robots for behavioral practices for mental health.

Abstract Social robots are increasingly ubiquitous in children’s lives, prompting questions regarding the promise and implications for children’s development. Social robots can be effective and helpful technological tools. Social robots are effective in supporting children’s learning in some domains, supporting better learning outcomes than with virtual agents and comparable to human tutors when tasks are simple and social. They also support the unique and individual needs of children with a range of special needs (e.g., autism spectrum disorder, hearing impairment) and promote children’s mental well-being and physical health. Yet social robots pose conceptual and ethical challenges in that they often present as if they have psychological and social characteristics. Consequently, children often understand social robots as mental, social, and moral entities, albeit to varying degrees depending upon children’s ages and robots’ features. Moreover, children treat robots in prosocial ways (e.g., helping, sharing) and believe robots deserve moral treatment. Yet, at the same time, some children deliberately abuse robots. Future research is needed to address critical questions and guide recommendations for the promise and limitations of social robots in children’s lives.

Stress detection in real-world settings presents significant challenges due to the complexity of human emotional expression influenced by biological, psychological, and social factors. While traditional methods like EEG, ECG, and EDA sensors provide direct measures of physiological responses, they are unsuitable for everyday environments due to their intrusive nature. Therefore, using non-contact, commonly available sensors like cameras and microphones to detect stress would be helpful. In this work, we use stress indicators from four key affective modalities extracted from audio-visual data: facial expressions, vocal prosody, textual sentiment, and physical fidgeting. To achieve this, we first labeled 353 video clips featuring individuals in monologue scenarios discussing personal experiences, indicating whether or not the individual is stressed based on our four modalities. Then, to effectively integrate signals from the four modalities, we extract stress signals from our audio-visual data using unimodal classifiers. Finally, to explore how the different modalities would interact to predict if a person is stressed, we compare the performance of three multimodal fusion methods: intermediate fusion, voting-based late fusion, and learning-based late fusion. Results indicate that combining multiple modes of information can effectively leverage the strengths of different modalities and achieve an F1 score of 0.85 for binary stress detection. Moreover, an ablation study shows that the more modalities are integrated, the higher the F1 score for detecting stress across all fusion techniques, demonstrating that our selected modalities possess complementary stress indicators.

In this work, we introduce a strategy that frames the sequential action selection problem for robots in terms of resolving blocking conditions, i.e., situations that impede progress on an action en route to a goal. This strategy allows a robot to make one-at-a-time decisions that take in pertinent contextual information and swiftly adapt and react to current situations. We present a first instantiation of this strategy that combines a state-transition graph and a zero-shot Large Language Model (LLM). The state-transition graph tracks which previously attempted actions are currently blocked and which candidate actions may resolve existing blocking conditions. This information from the state-transition graph is used to automatically generate a prompt for the LLM, which then uses the given context and set of possible actions to select a single action to try next. This selection process is iterative, with each chosen and executed action further refining the state-transition graph, continuing until the agent either fulfills the goal or encounters a termination condition. We demonstrate the effectiveness of our approach by comparing it to various LLM and traditional task-planning methods in a testbed of simulation experiments. We discuss the implications of our work based on our results.

In this work, we introduce a strategy that frames the sequential action selection problem for robots in terms of resolving \textit{blocking conditions}, i.e., situations that impede progress on an action en route to a goal. This strategy allows a robot to make one-at-a-time decisions that take in pertinent contextual information and swiftly adapt and react to current situations. We present a first instantiation of this strategy that combines a state-transition graph and a zero-shot Large Language Model (LLM). The state-transition graph tracks which previously attempted actions are currently blocked and which candidate actions may resolve existing blocking conditions. This information from the state-transition graph is used to automatically generate a prompt for the LLM, which then uses the given context and set of possible actions to select a single action to try next. This selection process is iterative, with each chosen and executed action further refining the state-transition graph, continuing until the agent either fulfills the goal or encounters a termination condition. We demonstrate the effectiveness of our approach by comparing it to various LLM and traditional task-planning methods in a testbed of simulation experiments. We discuss the implications of our work based on our results.

As robots are increasingly integrated into various aspects of everyday life, it becomes essential to develop intelligent systems capable of providing assistance while maintaining social appropriateness. In this paper, we challenge the prevailing assumption that robots should always offer help, prompting an essential discussion of when robots should offer help. We present a systematic way of considering socially appropriate assistance in human-robot interaction and introduce a theoretical framework that enables robots to discern whether or not to offer help to a human user. We examine the factors that influence the social appropriateness of help, including the relative skill levels between the robot and user and measures for assessing the social value and cost of help. Through a series of illustrative examples, we demonstrate the feasibility of our framework in providing socially appropriate assistance.

Gossip is a human behavior that has been shown to strengthen bonds, trust, and the feeling of inclusion between the gossiper and the person with whom they share the gossip. As humans engage more with social robots, fostering bonds between them is critical for meaningful interactions. In this paper, we investigated how gossiping can affect the perception of group inclusion and trust between a human and a robot. In this between-subjects user study (N = 38), we compared the effects of a robot that gossips to the participant in either a positive or negative way about the experimenter during an interaction. We found that participants in the positive condition reported a significant increase in group inclusion with the robot, while participants in the negative condition did not. We also found that participants’ moral trust in the negative condition significantly decreased. Our results suggested that positive gossip can be beneficial to human-robot team cohesion.

To enable sophisticated interactions between humans and robots in a shared environment, robots must infer the intentions and strategies of their human counterparts. This inference can provide a competitive edge to the robot or enhance human-robot collaboration by reducing the necessity for explicit communication about task decisions. In this work, we identify specific states within the shared environment, which we refer to as Critical Decision Points, where the actions of a human would be especially indicative of their high-level strategy. A robot can significantly reduce uncertainty regarding the human’s strategy by observing actions at these points. To demonstrate the practical value of Critical Decision Points, we propose a Receding Horizon Planning (RHP) approach for the robot to influence the movement of a human opponent in a competitive game of hide-and-seek in a partially observable setting. The human plays as the hider and the robot plays as the seeker. We show that the seeker can influence the hider to move towards Critical Decision Points, and this can facilitate a more accurate estimation of the hider’s strategy. In turn, this helps the seeker catch the hider faster than estimating the hider’s strategy whenever the hider is visible or when the seeker only optimizes for minimizing its distance to the hider.

We expect children to learn new words, skills, and ideas from various technologies. When learning from humans, children prefer people who are reliable and trustworthy, yet children also forgive people's occasional mistakes. Are the dynamics of children learning from technologies, which can also be unreliable, similar to learning from humans? We tackle this question by focusing on early childhood, an age at which children are expected to master foundational academic skills. In this project, 168 4-7-year-old children (Study 1) and 168 adults (Study 2) played a word-guessing game with either a human or robot. The partner first gave a sequence of correct answers, but then followed this with a sequence of wrong answers, with a reaction following each one. Reactions varied by condition, either expressing an accident, an accident marked with an apology, or an unhelpful intention. We found that older children were less trusting than both younger children and adults and were even more skeptical after errors. Trust decreased most rapidly when errors were intentional, but only children (and especially older children) outright rejected help from intentionally unhelpful partners. As an exception to this general trend, older children maintained their trust for longer when a robot (but not a human) apologized for its mistake. Our work suggests that educational technology design cannot be one size fits all but rather must account for developmental changes in children's learning goals.

Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit communicative signals from users to understand how they are being perceived during interactions. For example, these signals can be gaze patterns, facial expressions, or body motions that reflect internal human states. To facilitate future research in this direction, we contribute the \textttREACT database, a collection of two datasets of human-robot interactions that display users' natural reactions to robots during a collaborative game and a photography scenario. Further, we analyze the datasets to show that interaction history is an important factor that can influence human reactions to robots. As a result, we believe that future models for interpreting implicit feedback in HRI should explicitly account for this history. \textttREACT opens up doors to this possibility in the future.

Creating an accurate model of a user's skills is an essential task for Intelligent Tutoring Systems (ITS) and robotic tutoring systems. This allows the system to provide personalized help based on the user's knowledge state. Most user skill modeling systems have focused on simpler tasks such as arithmetic or multiple-choice questions, where the user's model is only updated upon task completion. These tasks have a single correct answer and they generate an unambiguous observation of the user's answer. This is not the case for more complex tasks such as programming or engineering tasks, where the user completing the task creates a succession of noisy user observations as they work on different parts of the task. We create an algorithm called Time-Dependant Bayesian Knowledge Tracing (TD-BKT) that tracks users' skills throughout these more complex tasks. We show in simulation that it has a more accurate model of the user's skills and, therefore, can select better teaching actions than previous algorithms. Lastly, we show that a robot can use TD-BKT to model a user and teach electronic circuit tasks to participants during a user study. Our results show that participants significantly improved their skills when modeled using TD-BKT.

A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.

Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit communicative signals from users to understand how they are being perceived during interactions. For example, these signals can be gaze patterns, facial expressions, or body motions that reflect internal human states. To facilitate future research in this direction, we contribute the REACT database, a collection of two datasets of human-robot interactions that display users' natural reactions to robots during a collaborative game and a photography scenario. Further, we analyze the datasets to show that interaction history is an important factor that can influence human reactions to robots. As a result, we believe that future models for interpreting implicit feedback in HRI should explicitly account for this history. REACT opens up doors to this possibility in the future.

This chapter introduces three exploratory studies that assessed the capabilities of artificial intelligence (AI) through standardised education tests designed for humans. The first two studies, conducted in 2016 and 2021/22, asked experts to evaluate AI’s performance on the literacy and numeracy tests of the OECD’s Survey of Adult Skills (PIAAC). The third study collected expert judgements of whether AI can solve science questions from the OECD&apos;s Programme for International Student Assessment (PISA). The studies aimed to refine the assessment framework for eliciting expert knowledge on AI using established educational assessments. They explored different test formats, response methodologies and rating instructions, along with two distinct assessment approaches. A “behavioural approach” used in the PIAAC studies emphasised smaller expert groups engaging in discussions, and a "mathematical approach" adopted in the PISA study relied more heavily on quantitative data from a larger expert pool. This chapter presents the results of the studies and discusses the advantages and disadvantages of their methodological approaches.

Social robots are in a unique position to aid mental health by supporting engagement with behavioral interventions. One such behavioral intervention is the practice of deep breathing, which has been shown to physiologically reduce symptoms of anxiety. Multiple robots have been recently developed that support deep breathing, but none yet implement a method to detect how accurately an individual is performing the practice. Detecting breathing phases (i.e., inhaling, breath holding, or exhaling) is a challenge with these robots since often the robot is being manipulated or moved by the user, or the robot itself is moving to generate haptic feedback. Accordingly, we first present OMMDB: a novel, multimodal, public dataset made up of individuals performing deep breathing with an Ommie robot in multiple conditions of robot ego-motion. The dataset includes RGB video, inertial sensor data, and motor encoder data, as well as ground truth breathing data from a respiration belt. Our second contribution features experimental results with a convolutional long-short term memory neural network trained using OMMDB. These results show the system’s ability to be applied to the domain of deep breathing and generalize between individual users. We additionally show that our model is able to generalize across multiple types of robot ego-motion, reducing the need to train individual models for varying human-robot interaction conditions.

Social robots in the home will need to solve audio identification problems to better interact with their users. This article focuses on the classification between (a) natural conversation that includes at least one co-located user and (b) media that is playing from electronic sources and does not require a social response, such as television shows. This classification can help social robots detect a user’s social presence using sound. Social robots that are able to solve this problem can apply this information to assist them in making decisions, such as determining when and how to appropriately engage human users. We compiled a dataset from a variety of acoustic environments that contained either natural or media audio, including audio that we recorded in our own homes. Using this dataset, we performed an experimental evaluation on a range of traditional machine learning classifiers and assessed the classifiers’ abilities to generalize to new recordings, acoustic conditions, and environments. We conclude that a C-Support Vector Classification (SVC) algorithm outperformed other classifiers. Finally, we present a classification pipeline that in-home robots can utilize, and we discuss the timing and size of the trained classifiers as well as privacy and ethics considerations.

Parsing the promise of language-capable robots.