Becoming Cyborg: Interdisciplinary Approaches for Exoskeleton Research

One of the challenges of outdoor robots is developing effective portable Human-Robot-Interaction (HRI) frameworks. Hand-held devices offer a practical solution. By equipping these devices with robot software, they can be made to interact with the outdoor robots. Android devices are ideal as they are open source and can be integrated with robots powered by the Robot Operating System (ROS), also open source. However, due to the limits of rosjava, the mechanism that links ROS with android, and the conflicting modes of operation between ROS and android, current implementations of ROS-android offer limited robot applications that do not support advanced operations such as autonomous navigation and others. This paper implements selective compartmentalization to overcome these limitations, by combining ROS with android through a number of ROS and android bridges that would facilitate the development of advanced robot applications. Through the proposed method, authors were able to develop a portable HRI framework that allowed human operators to supervise an outdoor mobile robot while it performed an autonomous task. From their mobile devices, users were able to initialize the robot, configure its motion, and monitor its progress. Also, users were able to reprogram the robot to perform new tasks (not previously planned) through a creative use of features offered in the developed HRI framework. Also, user cognitive effort was reported to be low as evident by the positive score on the NASA-TLX scale test which was corroborated with robot performance data. This paper presents the detailed development and implementation steps.

This scientometric study provides a comprehensive analysis mapping the geographic power structure that governs human-robot interaction (HRI) research. This paper analyses the geographic distribution of HRI research from 2005 to 2025 (n = 4,461 publications) using Scopus-indexed data from six core HRI venues. The findings reveal a research field characterised by profound geographic concentration and scientific oligarchy, with Europe and North America collectively accounting for 78.9% of publications and 79.2% of citations. The analysis identifies three structural paradoxes that define the field’s economy: (1) a ‘Maturation Paradox’ emerging from the tension between rapid volume growth and declining impact among established leaders; (2) a ‘Collaboration Paradox’ where international partnerships yield asymmetric benefits, favouring emerging regions while offering minimal gains for established leaders; and (3) a ‘Concentration Paradox’, revealing the contradiction between HRI’s global aspirations and a consolidated power structure where influence remains fixed in culturally similar subregions. Trend analysis raises critical questions about intellectual diversity and how geographic concentration shapes the fundamental assumptions and future trajectories of HRI research.

Anthropomorphism represents a central theoretical term in social robotics and human robot interaction (HRI) research. However, the research into anthropomorphism displays several conceptual problems that translate into methodological shortcomings. Here we report the results of a scoping review, which we conducted in order to explore (i) how the notion of ‘anthropomorphism’ is understood in HRI and social robotics research, and (ii) which assessment tools are used to assess anthropomorphism. Three electronic databases were searched; two independent reviewers were involved in the screening and data extraction process; a total of 57 studies were included in the final review which encompassed 43 different robots and 2947 participants. Across studies, researchers used seven different definitions of anthropomorphism and most commonly assessed the phenomenon by use of amended versions of existing questionnaires (n = 26 studies). Alternatively, idiosyncratic questionnaires were developed (n = 17 studies) which, as a qualitative thematic analysis of the individual questionnaire items revealed, addressed nine distinct themes (such as attribution of shared intentionality, attribution of personality etc.). We discuss these results relative to common standards of methodological maturity and arrive at the conclusion that the scope and heterogeneity of definitions and assessment tools of anthropomorphism in HRI hinders cross-study comparisons, while the lack of validated assessment tools might also affect the quality of results. To nurture reflection on these methodological challenges and increase comparability within the field we conclude by offering a set of reporting guidelines for research on anthropomorphism, as a first constructive effort to facilitate a coherent theory of anthropomorphism.

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.

This article reviews the state of the art in social eye gaze for human-robot interaction (HRI). It establishes three categories of gaze research in HRI, defined by differences in goals and methods: a human-centered approach, which focuses on people's responses to gaze; a design-centered approach, which addresses the features of robot gaze behavior and appearance that improve interaction; and a technology-centered approach, which is concentrated on the computational tools for implementing social eye gaze in robots. This paper begins with background information about gaze research in HRI and ends with a set of open questions.

We are excited to welcome you to the Twelfth Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI 2017). The HRI Conference is a highly selective, single track, international meeting showcasing the best research in human-robot interaction, with roots in and broad participation from various communities of scholars, including but not limited to robotics, human-computer interaction, artificial intelligence, engineering, social and behavioral sciences, and design. The theme of this year's conference is "Smart Interaction," following our host city Vienna's ongoing "Smart City" initiative. Designed with increasingly intelligent capabilities, robots must utilize such intelligence also in their interactions with people to improve the effectiveness, naturalness, and intuitiveness of these interactions as well as their moral and ethical consequences. Achieving intelligence in interaction requires the multidisciplinary convergence of technical, social, and design perspectives. To represent this multidisciplinarity, we solicited and reviewed papers under four submission themes: "Human-Robot Interaction User Studies," "Technical Advances in Human-Robot Interaction," "Human-Robot Interaction Design," and "Theory and Methods in Human-Robot Interaction." Each submission theme was overseen by a dedicated theme chair and reviewed by a dedicated group of program committee members, who worked together with the program chairs to define and apply review criteria appropriate to each of the four contribution types. The conference attracted 211 submissions from 38 unique countries from Asia-Pacific, Europe, the Middle East, and North America. Each full paper was aligned with a theme-appropriate subcommittee, and subsequently reviewed through a double-blind process, which was followed by a rebuttal phase, and shepherding where found appropriate by the program committee. As a result of the review process, the program committee selected 51 (24.2%) of the submissions for presentation as full papers at the conference. As the conference is jointly sponsored by IEEE and ACM, papers are archived in both the ACM Digital Library and the IEEE Xplore. Along with the full papers, the conference program and proceedings include the Late Breaking Reports, Videos, Demos, and an alt.HRI section. Out of 144 total submissions, 142 (98.6%) Late Breaking Reports (LBRs) were accepted and will be presented as posters at the conference. A new peer-review process ensured that authors of LBR submissions received detailed feedback on their work. Sixteen (80.0%) short videos were accepted for presentation during a dedicated video session from 20 submissions. The program also includes 12 demos of robot systems that participants will have an opportunity to interact with during the conference. We continue to include an alt.HRI session in this year's program, consisting of 4 papers (selected out of 19 submissions) that push the boundaries of thought and practice in the field. This year, we have reintroduced the Student Design Competition event to encourage student participation in the conference and enrich the program with novel ideas and insights developed by student teams. Finally, we have the pleasure of presenting three inspiring keynote speakers who will discuss topics relevant to HRI: Dr. Cynthia Breazeal from the MIT Media Lab and Jibo Inc. in the USA, Dr. Lucia Jacobs from the University of California, Berkeley in the USA, and Dr. Danica Kragic from the Royal Institute of Technology (KTH) in Sweden. HRI 2017 was made possible through the significant volunteer efforts of the organizing committee, program committee, reviewers, and the steering committee. We thank the keynote speakers, financial supporters, and international reviewers for their support and participation. The conference is sponsored by ACM SIGCHI, ACM SIGAI, IEEE Robotics and Automation Society, and is in cooperation with AAAI and HFES.

As Human-Robot Interaction (HRI) develops, robots are expected to learn more complex and demanding interaction skills. Complex HRI tasks are often embodied in robots that are jointly constrained by task space and joint space. In the field of Imitation Learning, scholars have explored the topic of joint constraints from task space and joint space, but there are few relevant studies in Human-Robot Interaction. In this paper, based on the Interaction Primitives framework (a HRI framework), we propose an interaction inference method that first generalizes the robot's movements in two spaces synchronously and then probabilistically fuses the two movements based on Bayesian estimation. This work was validated in the task that the robot follows a human handheld object, and the inference errors (RMSE and MAE) of the method are smaller in both task space and joint space than in IP using only singlespace inference.

In the process of human-robot interaction (HRI), the controleffect cannot meet the needs of HRI tasks if a control strategy is developed solely from the robot's point of view. It's necessary to take into account the characteristics of the human operator. In this letter, a novel HRI framework is developed to realise robot humanoid control by transferring the human arm stiffness and human hand motion characteristics to the robot. Firstly, adaptive impedance control is used to adjust the robot's stiffness through human arm active endpoint stiffness, which is determined by the geometric information and muscle activation of the human arm. In addition, a direction adaptive compensation method is also proposed by introducing a direction compensating quantity to regulate the robot's trajectory for minimising the effect on the robot of changes in the motion direction of the human hand. The proposed method can not only stabilize the process of HRI but also decrease the interaction force to reduce the human operator's workload. Comparative experiments demonstrate the enhanced performance of the proposed method.

Current designerly research in human–robot interaction (HRI) is often exploring how to reframe HRI research toward designerly approaches that are more situated, reflective, critical, and inclusive. While arguably underrepresented in the flagship conference and journal in the field (i.e., ACM/IEEE HRI Conference, ACM Transaction on HRI), such work is concerned to pose and examine deeper critical questions about the design and use of robots in society as lenses into a bigger phenomenon. Connecting with Chapter 6, in this chapter Ljungblad and Gamboa, provide an overview of the current socio-technical discourse around critical approaches in HRI. To this end, they introduce Critical Robotics, a conceptual structure aiming to support the formation of alternative research approaches and strengthen alternative epistemological grounds for HRI research, and propose topics for future critical research in HRI.

As social robots rapidly become mainstream tech- nologies, it is critical for HRI researchers and practitioners to consider their societal and ethical impacts as well as their ability to perpetuate or mitigate intersectional social inequities and hierarchies relating to race, class, gender, disability, and other social axes. Through an equity, ethics, and justice- centered audit of human-robot interaction (HRI) scholarship, we reveal how the HRI community has engaged with these topics over the past two decades. We use the five senses ethical framework that has been proposed specifically for use in HRI contexts to perform the review paired with an analysis of equity and justice. We then expand the Design Justice framework (a framework for analyzing how design impacts society and distributes benefits and burdens to society through the lenses of equity, values, scope, ownership, and accountability) to HRI contexts through the inclusion of HRI-specific topics such as autonomy, transparency, deception, and policies. We invite researchers and practitioners to explore the HRI Equitable Design framework to work towards designing equitable and inclusive HRI research studies and technologies.

Sensor data taken during a human-robot interaction (HRI) have high potential for usage as new, objective measures of an interaction, either replacing or supplementing survey techniques that are currently most common in HRI research. Sensor data can be taken in large quantities quickly, naturally, and discreetly. They also have the potential to reflect a user’s biosignals—information about the user’s inner state (such as stress and attention) when interacting with the robot. We previously conducted three studies attempting to use sensor data as a measurement in HRI, with methodological differences in three different experimental environments. In this paper, we reanalyze and add new data to the previous findings under a consistent methodology, consolidate what correlations we find, and can conclude that sensor data is a useful metric in HRI across a wide range of experimental setups and subject pools. We fully describe the methodology we determined to be most effective, from selection of sensors to data analysis techniques to HRI experiment setup, as a basis for how this methodology can be used in other HRI studies. We describe necessary steps in the analysis of a large amount of sensor data (over 100,000 sets) and how sensor data can be compared with survey and behavioral data. Based on these correlations, we find that the most effective sensors are temperature sensors, tactile sensors, and face distance measurements. We also find that higher measurements across all of these sensors are more correlated with both survey and behavioral measurements reflecting positive thinking towards a robot (including non-technophobia, reciprocal behaviors, and positive ratings of the robot) during an interaction. Based on these results, we argue that robot sensor usage is an important and objective metric for HRI research.

As social robots rapidly become mainstream technologies, it is critical for HRI researchers and practitioners to consider their societal and ethical impacts as well as their ability to perpetuate or mitigate intersectional social inequities and hierarchies relating to race, class, gender, disability, and other social axes. Through an equity, ethics, and justice-centered audit of human-robot interaction (HRI) scholarship, we reveal how the HRI community has engaged with these topics over the past two decades. We use the five senses ethical framework that has been proposed specifically for use in HRI contexts to perform the review paired with an analysis of equity and justice. We then expand the Design Justice framework (a framework for analyzing how design impacts society and distributes benefits and burdens to society through the lenses of equity, values, scope, ownership, and accountability) to HRI contexts through the inclusion of HRI-specific topics such as autonomy, transparency, deception, and policies. We invite researchers and practitioners to explore the HRI Equitable Design framework to work towards designing equitable and inclusive HRI research studies and technologies.

In recent years, there has been a rapid rise in new product innovations in robots, robotic devices, human-robot interfaces, and social robots across the globe. This has largely been due to several inter-disciplinary research & development output from industries as well as a few robotics research institutions who have successfully embraced the convergence of braintech, robotics engineering, biomedical engineering, artificial intelligence, machine learning, and novel platform in human-machine interactions. However, the rate and scale of innovation in human-robot interaction (HRI) and their impact in creating social goods is still much slower compared to similar developments in other fields of research. We surmise that HRI innovation and commercialization of HRI products present unique challenges that are typically not encountered in other industries. Fragmented research, lack of supporting infrastructure and the gaps between lab research and user needs; as well as lack of venture capital investment in hardware products have led to sluggish innovation and longer duration of bringing those innovation into the real-world market. In this paper, we discuss the unique set of challenges faced by current HRI research/innovation and propose some methodology to direct the academic intellectual resources towards faster commercialization of their research.

Research on Human-Robot Interaction (HRI) requires the substantial consideration of an experimental design, as well as a significant amount of time to practice the subject experiment. Recent technology in virtual reality (VR) can potentially address these time and effort challenges. The significant advantages of VR systems for HRI are: 1) cost reduction, as experimental facilities are not required in a real environment; 2) provision of the same environmental and embodied interaction conditions to test subjects; 3) visualization of arbitrary information and situations that cannot occur in reality, such as playback of past experiences, and 4) ease of access to an immersive and natural interface for robot/avatar teleoperations. Although VR tools with their features have been applied and developed in previous HRI research, all-encompassing tools or frameworks remain unavailable. In particular, the benefits of integration with cloud computing have not been comprehensively considered. Hence, the purpose of this study is to propose a research platform that can comprehensively provide the elements required for HRI research by integrating VR and cloud technologies. To realize a flexible and reusable system, we developed a real-time bridging mechanism between the robot operating system (ROS) and Unity. To confirm the feasibility of the system in a practical HRI scenario, we applied the proposed system to three case studies, including a robot competition named RoboCup@Home. via these case studies, we validated the system’s usefulness and its potential for the development and evaluation of social intelligence via multimodal HRI.

Human-robot interaction (HRI) has been maturing in tandem with robots’ commercial success. In the last few years HRI researchers have been adopting—and sometimes adapting—human-computer interaction (HCI) evaluation techniques to assess the efficiency and intuitiveness of HRI designs. For example, Adams (2005) used Goal Directed Task Analysis to determine the interaction needs of officers from the Nashville Metro Police Bomb Squad. Scholtz et al. (2004) used Endsley’s (1988) Situation Awareness Global Assessment Technique to determine robotic vehicle supervisors’ awareness of when vehicles were in trouble and thus required closer monitoring or intervention. Yanco and Drury (2004) employed usability testing to determine (among other things) how well a search-andrescue interface supported use by first responders. One set of HCI tools that has so far seen little exploration in the HRI domain, however, is the class of modeling and evaluation techniques known as formal methods.