Humanoid robots are increasingly expected to be designed to interact in diverse social contexts. Yet, recent commercial prototypes often omit expressive communication features, such as faces and gestures. This disconnect raises questions about how communication modalities are actually selected for real-world deployment. In this study, we interviewed 12 industry decision-makers involved in humanoid robot development to examine the factors that guide their design adoption choices. Our findings show that technical feasibility, cost, safety, reliability, and organizational priorities frequently outweigh the benefits of communication modalities. By highlighting where practitioner considerations align with or diverge from established design research, this study offers a grounded, industry-facing perspective on the design of humanoid communication. Interpreting our findings, we present a conceptual framework to guide design researchers in understanding decision-making for product development in industry contexts.

The comparatively recent advent of Large Language Models (LLMs) has resulted in a wide array of new capabilities and components relevant to Human–Robot Interaction (HRI) researchers. LLMs are being applied to vision, manipulation, planning, reasoning, learning, and HRI problems, frequently as “Scarecrows,” in which LLMs serve as black box modules integrated into robot architectures for the purpose of quickly enabling full-pipeline solutions. However, despite this explosion of applications, general questions remain about the best ways to incorporate LLMs into robot architectures, appropriate safety and guardrail considerations, and, critically, how to report properly on HRI research that involves LLMs. In this article, we explore the question of reporting guidelines for HRI researchers who utilize Scarecrows in robot architectures. We identify five key stakeholder groups in the HRI research process, discuss what information each group needs from HRI researchers, and identify appropriate mechanisms for conveying that information from HRI researchers to stakeholders either directly or indirectly. We contribute a set of suggested guidelines regarding what information should be included when researchers disseminate information about HRI research that uses LLMs.

The proliferation of Large Language Models (LLMs) presents both a critical design challenge and a remarkable opportunity for the field of Human–Robot Interaction (HRI). While the direct deployment of LLMs on interactive robots may be unsuitable for reasons of ethics, safety, and control, LLMs might nevertheless provide a promising baseline technique for many elements of HRI. Specifically, in this article, we argue for the use of LLMs asScarecrows: “brainless,” straw-man black-box modules integrated into robot architectures for the purpose of quickly enabling full-pipeline solutions, much like the use of “Wizard of Oz” (WoZ) and other human-in-the-loop approaches. We explicitly acknowledge that these Scarecrows, rather than providing a satisfying or scientifically complete solution, incorporate a form of the wisdom of the crowd and, in at least some cases, will ultimately need to be replaced or supplemented by a robust and theoretically motivated solution. We provide examples of how Scarecrows could be used in language-capable robot architectures as useful placeholders and suggest initial reporting guidelines for authors, mirroring existing guidelines for the use and reporting of WoZ techniques.

The drive for increasing adoption of HRI technolo-gies is evident through research and development of manufac-turing, social, medical, and service robot solutions. However, novel methods and metrics are required to overcome the barrier between fundamental HRI research and its adoption in real-world environments. Hence, the fourth installment of the annual workshop, 'Test Methods and Metrics for Effective HRI,’ seeks to identify novel and emerging test methods and metrics for the holistic assessment and assurance of HRI performance. Specifically, the focus is on identifying innovative methods for the evaluation of HRI performance and to advance the growth of the HRI community based on the principles of collaboration, data sharing, and repeatability. The goal of this workshop is to break the boundaries between the development and adoption of HRI technologies through the promotion of robust experimental design, test methods, and metrics for assessing interaction and interface designs. This workshop will have participants from var-ious sectors in the HRI research community including academia, industry, and government in order to accomplish its aims.

With robotics rapidly advancing, more effective human–robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be part of the solution, our ability to provide spoken language interaction capabilities is still very limited. In this article, based on the report of an interdisciplinary workshop convened by the National Science Foundation, we identify key scientific and engineering advances needed to enable effective spoken language interaction with robotics. We make 25 recommendations, involving eight general themes: putting human needs first, better modeling the social and interactive aspects of language, improving robustness, creating new methods for rapid adaptation, better integrating speech and language with other communication modalities, giving speech and language components access to rich representations of the robot’s current knowledge and state, making all components operate in real time, and improving research infrastructure and resources. Research and development that prioritizes these topics will, we believe, provide a solid foundation for the creation of speech-capable robots that are easy and effective for humans to work with.

This special issue of the Transactions on Human-Robot Interaction highlights, documents, and explores the metrics, test methods, and artifacts used in human-robot interaction (HRI) research. This collection of articles brings to attention the commonalities between the application of measurement science for the assessment and assurance of human-centric robotics in a variety of application domains, including industry, education, and defense. This special issue draws specific attention to the use and impact of metrology toward the advancement of HRI technologies and algorithms, and it promotes the application of measurement science toward the benchmarking and replication of HRI research. Special attention is given to the use cases, data sets, test methodologies, measurement techniques, metrics, and statistical analyses used to evaluate system performance.

Hardware platforms for socially interactive robotics can be limited by cost or lack of functionality. This article presents the overall system—design, hardware, and software—for Quori, a novel, affordable, socially interactive humanoid robot platform for facilitating noncontact human–robot interaction (HRI) research. The design of the system is motivated by feedback sampled from the HRI research community. The overall design maintains a balance of affordability and functionality. Initial Quori testing and a six-month deployment are presented. Ten Quori platforms have been awarded to a diverse group of researchers from across the United States to facilitate HRI research to build a community database from a common platform.

This work investigates the deployment of an affordable socially assistive robot (SAR) at an older adult day care setting for the screening of COVID-19 symptoms and exposure. Despite the focus on older adults, other stakeholders (clinicians and caregivers) were included in the study due to the need for daily COVID-19 screening. The investigation considered which aspects of human-robot-interaction (HRI) are relevant when designing social agents for patient screening. The implementation was based upon the current screening procedure adopted by the deployment facility, and translated into robot dialogues and gesturing motion. Post-interaction surveys with participants informed their preferences for the type of interaction and system usability. Observer surveys evaluated users’ reaction, verbal and physical engagement. Results indicated general acceptance of the social agent and possible improvements to the current version of the robot to encourage a broader adoption by the stakeholders.

Verified and validated test methods, being necessary to measure the performance of complex systems, are important tools for driving innovation, benchmarking and improving performance, and establishing trust in collaborative human-robot teams. This full-day workshop aims to explore the metrology necessary for repeatably and independently assessing the collaborative performance of robotic systems in real-world human-robot interaction (HRI) scenarios. This workshop aims to bridge the gaps between the theory and applications of HRI in industry, accelerating the adoption of cutting edge technologies as the industry state-of-practice. The interest in collaborative HRI is evident in the current market as well as standards efforts toward manufacturing, social, medical, and service robot solutions. Though these domains have been considered separate for many years, recent technological and scientific advancements show that, while their applications may differ, the underlying principles of HRI performance impact each identically. As such, this workshop seeks to identify test methods and metrics for the holistic assessment and assurance of collaborative HRI performance. The focus is on identifying the key performance indicators of these seemingly disparate sectors, and additionally to establish a community based on the principles of transparency, repeatability, & establishing trust in the assessment of collaborative HRI. The goal is to aid in the advancement of HRI technologies through the development of experimental scenarios, protocols, test methods, & metrics for the verification and validation of interaction solutions and interface designs.

The use of immersive Virtual Reality (VR) for studying Human-Robot Interaction (HRI) offers many benefits, including decreased cost and risk as well as increased experimental control and repeatability. Previous work has shown that people reliably underestimate distances in VR; however, the effect of this underestimation on gesture recognition has not been characterized. This work contributes to the validation of immersive VR as a platform for HRI investigation and simulation for training in industry. A matched pair of studies compared the location preferences of human participants when viewing gestures generated by a robot in both virtual and physical environments. Participants were asked to select up to three optimal locations within a bounded region at which they perceived the robot's gesture to be the clearest. We found that the use of VR did increase the preferred proxemic distance (χ2(1) = 18.046, p <; 0.001) by approximately 642 ± 96mm. The difference in viewing angle between the virtual and physical environments was not significant, with a 95% confidence interval limiting the difference within -8.6° to +7.9°. Observations relating gesture features to optimal viewing locations are also presented.

Our approach enables the robot to execute proxemic behaviors that improve speech and gesture recognition for both the robot and the human, even in noisy and cluttered environments. This work has been deployed on two different mobile robot platforms: (1) the PR22, and (2) the Bandit3upper body humanoid atop an iRobot Ava4mobile base. This demonstrates the portability of the approach.

Human preferences of distance (proxemics) to a robot significantly impact the performance of the robot's automated speech and gesture recognition during face-to-face, social human-robot interactions. This work investigated how people respond to a sociable robot based on its performance at different locations. We performed an experiment in which the robot's ability to understand social signals was artificially attenuated by distance. Participants (N = 180) instructed the robot using speech and pointing gestures, provided proxemic preferences before and after the interaction, and responded to a questionnaire. Our analysis of questionnaire responses revealed that robot performance factors---rather than human-robot proxemics---are significant predictors of user evaluations of robot competence, anthropomorphism, engagement, likability, and technology adoption. Our behavioral analysis suggests that human proxemic preferences change over time as users interact with and come to understand the needs of the robot, and those changes improve robot performance.

An objective of an autonomous sociable robot is to meet the needs and preferences of a human user. However, this can sometimes be at the expense of the robot's own ability to understand social signals produced by the user. In particular, human preferences of distance (proxemics) to the robot can have significant impact on the performance rates of its automated speech and gesture recognition systems. In this work, we investigated how people perceive a sociable robot based on its performance at different locations. We performed an experiment in which a robot's ability to understand social signals was artificially attenuated across distance; robot maximum, minimum, and average performance rates were also varied. Participants (N = 160) instructed a robot using speech and pointing gestures, and then responded to a questionnaire to provide their subjective evaluations of the robot.We identified significant relationships between robot performance and user perceptions of robot competence, anthropomorphism, engagement, likability, and technology adoption; we identified no relationship between human-robot distance and subjective measures, which contrasts related work. Our results have significant implications for autonomous sociable robot design.

Every year, tens of thousands of middle school and high school students participate in robotics competitions, such as Botball, FIRST, and VEX. This provides them with an excellent introduction to the ins and outs of building robots and programming them to autonomously accomplish specific tasks. However, the rules of many of these competitions often limit or prohibit human interaction with the robots. As a result, students are not exposed to and are, thus, not encouraged to think about human-robot interaction (HRI) and its potential impacts on society.

In this paper, we draw upon insights gained in our previous work on human-human proxemic behavior analysis to develop a novel method for human-robot proxemic behavior production. A probabilistic framework for spatial interaction has been developed that considers the sensory experience of each agent (human or robot) in a co-present social encounter. In this preliminary work, a robot attempts to maintain a set of human body features in its camera field-of-view. This methodology addresses the functional aspects of proxemic behavior in human-robot interaction, and provides an elegant connection between previous approaches.

To enable natural and productive situated human-robot interaction, a robot must both understand and control proxemics, the social use of space, in order to employ communication mechanisms analogous to those used by humans: social speech and gesture production and recognition. My research focuses on answering these questions: How do social (auditory and visual) and environmental (noisy and occluding) stimuli influence spatially situated communication between humans and robots, and how should a robot dynamically adjust its communication mechanisms to maximize human perceptions of its social signals in the presence of extrinsic and intrinsic sensory interference?

This full day workshop aims to bring together researchers from the embodied conversational agent (ECA) and sociable robotics communities to spark discussion and collaboration between the related fields. The focus of the workshop is on co-verbal behavior production -- specifically, synchronized speech and gesture -- for either virtually or physically embodied platforms. It elucidates the subject in consideration of aspects regarding planning and realization of multimodal behavior production. Topics discussed highlight common as well as distinguishing factors of their implementations within each respective field.

We present a user study and dataset designed and collected to analyze how humans use space in face-to-face interactions. In a proof-of-concept investigation into human spatial dynamics, a Hidden Markov Model (HMM) was trained over a subset of features to recognize each of three interaction cues - initiation, acceptance, and termination - in both dyadic and triadic scenarios; these cues are useful in predicting transitions into, during, and out of multi-party social encounters. It is shown that the HMM approach performed twice as well as a weighted random classifier, supporting the feasibility of recognizing and predicting social behavior based on spatial features.

In many collocated human-robot interaction scenarios, robots are required to accurately and unambiguously indicate an object or point of interest in the environment. Realistic, cluttered environments containing many visually salient targets can present a challenge for the observer of such pointing behavior. In this paper, we describe an experiment and results detailing the effects of visual saliency and pointing modality on human perceptual accuracy of a robot's deictic gestures (head and arm pointing) and compare the results to the perception of human pointing.

As a robot formation increases in size or explores places where it is difficult for a human operator to interact, autonomous control becomes critical. We propose a distributed autonomous method for evaluating properties of multi-robot systems, and then discuss how this information can be applied to improve performance with respect to a given operation. We present this as an extension of our previous work on robot formations; however, the techniques described could be adapted to other multi-robot systems.

The field of multi-robot coordination, specifically robot formation control, is rapidly expanding, with many applications proposed. In our previous work, we considered the problem of establishing and maintaining a formation of robots given an already connected network. We now propose a distributed auction-based method to autonomously initialize and reorganize the network structure of a formation of robots.

New approaches to rehabilitation and health care have developed due to advances in technology and human robot interaction (HRI). Socially assistive robotics (SAR) is a subcategory of HRI that focuses on providing assistance through hands-off interactions. We have developed a SAR architecture that facilitates multiple task-oriented interactions between a user and a robot agent. The architecture accommodates a variety of inputs, tasks, and interaction modalities that are used to provide relevant, real-time feedback to the participant. We have implemented the architecture and validated its technological feasibility in a small pilot study in which a SAR agent led three post-stroke individuals through an exercise scenario. In the following, we present our architecture design, and the results of the feasibility study.

We present a preliminary implementation of a robot within the context of social skills intervention. The robot engages a human user in an interactive and adaptive game-playing session that emphasizes a specific sequence of movements over time. Such games highlight joint attention and encourage forms of interaction that are useful within various assistive domains. Noteworthy robot activities include those that could be used to promote social cues in children with autism, sequences that maintain or improve memory in Alzheimer's patients, and movements that encourage exercises to increase range of motion in post-stroke rehabilitation.

The goal of a robot formation control architecture is to get a number of robots into a specified form. To be effective and practical, the control architecture must be able to transition a group of robots from an initial swarm to a final formation. It must then be able to handle real-world events that could disrupt the formation, thus, requiring formation repair, obstacle avoidance, and changes in the formation. In previous work, we presented a distributed, reactive cellular automata-based formation control architecture capable of controlling any number of robots in formation at once. In this paper, we examine our architecture with respect to necessary characteristics to handle real-world occurrences. To address issues of formation repair and obstacle avoidance, the control architecture is extended by a distributed auctioning method that allows the robot formation to reconfigure autonomously.