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.

Robots are often designed to help, but help is not always helpful. In everyday situations, it is a socially delicate act: the right offer of help at the wrong moment can be intrusive, unnecessary, or even undermining. In this article, we challenge the prevailing assumption that robots should always offer help, prompting an essential discussion of how robots can discern when to offer help. We introduce a theoretical framework that enables robots to assess the appropriateness of offering help by considering factors such as the relative skill levels of the robot and human user, as well as the social value and cost of assistance. To validate this framework, we conducted a large-scale online study in which participants rated the appropriateness of robot assistance across diverse task scenarios. Their responses supported our core predictions and highlighted additional contextual factors. Building on these results, we discuss potential extensions of the simplified model for real-world settings, including uncertainty management, perception of ability, autonomy preferences, and social presence. We present these directions as opportunities for future research.

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.

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.

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.

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.

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.

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.

A growing population of adults with Autism Spec-trum Disorders (ASD) chronically struggles to find and maintain employment. Previous work reveals that one barrier to employment for adults with ASD is dealing with workplace interruptions. In this paper, we present our design and evaluations of an in-home autonomous robot system that aims to improve users' tolerance to interruptions. The Interruptions Skills Training and Assessment Robot (ISTAR) allows adults with ASD to practice handling interruptions to improve their employability. ISTAR is evaluated by surveys of employers and adults with ASD, and a week-long study in the homes of adults with ASD. Results show that users enjoy training with ISTAR, improve their ability to handle various work-relevant interruptions, and view the system as a valuable tool for improving their employment prospects.

The practice of social distancing during the COVID-19 pandemic resulted in billions of people quarantined in their homes. In response, we designed and deployed VectorConnect, a robot teleoperation system intended to help combat the effects of social distancing in children during the pandemic. VectorConnect uses the off-the-shelf Vector robot to allow its users to engage in physical play while being geographically separated. We distributed the system to hundreds of users in a matter of weeks. This paper details the development and deployment of the system, our accomplishments, and the obstacles encountered throughout this process. Also, it provides recommendations to best facilitate similar deployments in the future. We hope that this case study about Human-Robot Interaction practice serves as an inspiration to innovate in times of global crises.