For social robots to succeed in human environments, they must respond in effective yet appropriate ways when humans violate social and moral norms, e.g., when humans give them unethical commands. Humans expect robots to be competent and proportional in their norm violation responses, and there are a wide range of strategies robots could use to tune the politeness of their utterances to achieve effective, yet appropriate responses. Yet it is not obvious whether all such strategies are suitable for robots to use. In this work, we assess a robot's use of human-like Face Theoretic linguistic politeness strategies. Our results show that while people expect robots to modulate the politeness of their responses, they do not expect them to strictly mimic human linguistic behaviors. Specifically, linguistic politeness strategies that use direct, formal language are perceived as more effective and more appropriate than strategies that use indirect, informal language.

Wizard-of-Oz (WoZ) is one of the most widely used experimental methodologies across the field of Human-Robot Interaction (HRI), making WoZ teleoperation interfaces a critical tool for HRI research. Yet current WoZ teleoperation interfaces are overwhelmingly tailored towards a narrow set of HRI interaction paradigms. In this work, we conducted a set of interviews with HRI researchers to better understand the diversity of teleoperation needs across the HRI community. Our analysis highlighted (1) human challenges, with respect to wizards’ expertise, the need for quick responses, and research participants’ unpredictability; (2) robot challenges, with respect to robot malfunctions, delays, and robot-driven complexity, and (3) interaction challenges, with respect to researchers’ varying control requirements and the need for precise experimental control. Moreover, our results revealed unexpected parallels between the experiences of HRI researchers and real-world teleoperators, which open up fundamentally new possibilities for future work in robot control interfaces and encourage radically different perspectives on what types of interfaces are even needed to best facilitate WoZ experimentation. Leveraging these insights, we recommend that WoZ interfaces (1) be designed with extensibility and customization in mind, (2) ease interaction management by accounting for unpredictability and multi-robot interactions, and (3) consider WoZ teleoperators beyond the context of experimentation.

To critically analyze and adapt to the risks and benefits of social robotics, future user communities will require technology and AI literacy: the ability to use new robotic technologies, understand their strengths and limitations, and critically evaluate the implications of their use. Research shows that collaborative, creative, and informal learning experiences can support AI literacy among non-technologists. Therefore, we designed Degrees of Freedom, a multiplayer interactive storytelling game that supports technology literacy about social robots. Degrees of Freedom supports technology literacy competencies by encouraging players to explore how values are encoded in robot designs, compelling players to consider the risks and limitations of robots, and encouraging them to make connections to their own lives and values. We present both the design of Degrees of Freedom and the results of game playtesting. Our results show that the narrative, collaborative nature of the game supported players in critical thinking about the role robots can or should have in their communities.

As robots become increasingly used in space exploration, it is important to ensure that space robots are developed with the appropriate level of autonomy. Semiautonomous robots operating in space contexts face unique challenges, as these robots often operate in situations that may be safety-critical, environments that are not fully known, and with communication delay to operators on Earth. Due to these challenges, there exist both advantages and risks to developing systems with high levels of autonomy to operate in space contexts. Therefore, we aim to investigate perspectives on the trade-offs of increased autonomy for space robotic systems and the human factors considerations that should be evaluated when designing these systems. We conducted qualitative interviews with five professionals in the space robotics industry to explore these perspectives. Our findings demonstrate that decisions regarding the level of autonomy of space robots are shaped not only by technical considerations, but also by operators’ willingness to accept new technology, financial considerations, and even human operators’ sense of control. Based on these results, we present design recommendations for roboticists and human factors engineers in the space robotics domain.

In this paper, we explore how robots can properly explain failures during navigation tasks with privacy concerns. We present an integrated robotics approach to generate visual failure explanations, by combining a language-capable cognitive architecture (for recognizing intent behind commands), an object- and location-based context recognition system (for identifying the locations of people and classifying the context in which those people are situated) and an infeasibility proof-based motion planner (for explaining planning failures on the basis of contextually mediated privacy concerns). The behavior of this integrated system is validated using a series of experiments in a simulated medical environment.

Wilderness search and rescue teams face challenges in hazardous environments. While robots show promise for these teams, their success depends on their ability to account for sociotechnical considerations, including human factors, as well as the organizational, economic, and emotional realities of search missions. We investigate these considerations through interviews with wilderness search team members who handle search dogs. These interviews reveal underexplored perspectives on awareness and uncertainty, the value of training experiences, team dynamics, and financial feasibility. Our findings motivate design recommendations for semiautonomous systems in the wilderness, yet also raise key questions regarding the role that robots can and should play in this domain.

Social robots will inevitably confront social or moral norm violations. While researchers have identified preliminary strategies for when and how robots should respond in such situations, it is not well understood how humans will make sense of these robot behaviors. We used qualitative, narrative-based methods inspired by design fiction to better understand how humans appraise these interactions. Our narrative survey invited participants to share their assumptions and expectations, analyze scenarios, and make suggestions. Our results highlight key situational and psychological factors that characterize norm-sensitive robot interactions, and suggest clear insights for the development of socially competent robot teammates.

Socially assistive robots play an effective role in children's therapy and education. Robots engage children and provide interaction that is free of the potential judgment of human peers and adults. Research in socially assistive robots for children generally focuses on therapeutic and educational outcomes for those children, informed by a vision of autonomous robots. This perspective ignores therapists and educators, who operate these robots in practice. Through nine interviews with individuals who have used robots to deliver socially assistive services to neurodivergent children, we (1) define a dual-cycle model of therapy that helps capture the domain expert view of therapy, (2) identify six core themes of teleoperator needs and patterns across these themes, (3) provide high-level guidelines and detailed recommendations for designing teleoperated socially assistive robot systems, and (4) outline a vision of robot-assisted therapy informed by these guidelines and recommendations that centers teleoperators of socially assistive robots in practice.

Socially Assistive Robots (SARs) show significant promise in a number of domains: providing support for the elderly, assisting in education, and aiding in therapy. Perhaps unsurprisingly, SAR research has traditionally focused on providing evidence for this potential. In this paper, we argue that this focus has led to a lack of critical reflection on the appropriate level of autonomy (LoA) for SARs, which has in turn led to blind spots in the research literature. Through an analysis of the past five years of HRI literature, we demonstrate that SAR researchers are overwhelmingly developing and envisioning autonomous robots. Critically, researchers do not include a rationale for their choice in LoA, making it difficult to determine their motivation for fully autonomous robots. We argue that defaulting to research fully autonomous robots is potentially short-sighted, as applying LoA selection guidelines to many SAR domains would seem to warrant levels of autonomy that are closer to teleoperation. We moreover argue that this is an especially critical oversight as teleoperated robots warrant different evaluation metrics than do autonomous robots since teleoperated robots introduce an additional user, the teleoperator. Taken together, this suggests a mismatch between LoA selection guidelines and the vision of SAR autonomy found in the literature. Based on this mismatch, we argue that the next five years of SAR research should be characterized by a shift in focus towards teleoperation and teleoperators.

Robots are increasingly present in ethically fraught childhood spaces. In such contexts, HRI researchers should leverage mixed-methods approaches. This is especially true in domains where robots are teleoperated by adult experts-such as therapy. A mixed-methods approach can help researchers build a thorough qualitative understanding of adult experts' needs, incorporate stakeholder perspectives through participatory design, and motivate experimental evaluations with this insight. Through such a user-centered, mixed-methods approach, robotics researchers can ultimately improve the experience of both adults and children in these spaces.

Children are stakeholders of robotic technologies who deserve to have their voices heard in the design process just as much as adult stakeholders. This is especially true for robotic technologies explicitly designed for child-robot interaction, in areas like education, healthcare, and therapy. Researchers face the challenge of cultivating children's critical awareness on the design of robots and accompanying ethical concerns, as the types of exercises typically used to engage with adult stakeholders can be ineffective with children. This requires developmentally appropriate methods for understanding children's perspectives that also address the imbalanced power dynamics between children and adults-such that children feel comfortable sharing their ideas. In this work, we demonstrate that participatory design research techniques already accepted in the Human Robot Interaction (HRI) community can fulfill this purpose. Specifically, through the design and analysis of two co-design workshops with children of different ages at a school in Denver, Colorado, we demonstrate that co-design workshops can be used to effectively understand how children make sense of robotic technologies and to facilitate children's critical reflection on the ethical dilemmas surrounding their own relationships with robots.

Socially Assistive Robots (SARs) have shown promise, but there are still practical challenges to their widespread adoption. Recent research has demonstrated the advantages of teleoperated systems in this space and called for better guidelines for teleoperation interfaces. We ran group usability tests with therapists with no experience with robots to learn more about the challenges they face. We found that robot-novice therapists understand how robots can be effective in therapy. However, learning to use a robot interface can be challenging for new users. These challenges include the unfamiliar metaphors used for robot connection and the need to create, acquire, or share robot interaction content. We also identify users’ needs that are perhaps non-obvious in a research context, such as privacy of client health information and professional boundaries with client families when using electronic tools. We make several recommendations based on analysis of our group usability tests: (1) developing dedicated interfaces for content authoring that account for caregiver technical expertise, (2) implementing content organization and sharing tools, (3) using connection metaphors that non-technical users may be more familiar with such as phone calls or web URLs, (4) considering user privacy in connection methods chosen, especially within telehealth. Most importantly, we encourage further research in SAR teleoperation that focuses on caregivers as teleoperators.

We consider the problem of multi-robot sensor coverage, which deals with deploying a multi-robot team in an environment and optimizing the sensing quality of the overall environment. As real-world environments involve a variety of sensory information, and individual robots are limited in their available number of sensors, successful multi-robot sensor coverage requires the deployment of robots in such a way that each individual team member’s sensing quality is maximized. Additionally, because individual robots have varying complements of sensors and both robots and sensors can fail, robots must be able to adapt and adjust how they value each sensing capability in order to obtain the most complete view of the environment, even through changes in team composition. We introduce a novel formulation for sensor coverage by multi-robot teams with heterogeneous sensing capabilities that maximizes each robot's sensing quality, balancing the varying sensing capabilities of individual robots based on the overall team composition. We propose a solution based on regularized optimization that uses sparsity-inducing terms to ensure a robot team focuses on all possible event types, and which we show is proven to converge to the optimal solution. Through extensive simulation, we show that our approach is able to effectively deploy a multi-robot team to maximize the sensing quality of an environment, responding to failures in the multi-robot team more robustly than non-adaptive approaches.