With advancement of robotics and artificial intelligence, applications for robotics are flourishing. Human-robot interaction (HRI) is an important area of robotics as it allows robots to work closer to humans (with them or for them). One crucial factor for the success of HRI research is transferability, which refers to the ability of research outputs to be adopted by industry and provide benefits to society. In this paper, we explore the potentials and challenges of transferability in HRI research. Firstly, we examine the current state of HRI research and identify various types of contributions that could lead to successful outcomes. Secondly, we discuss the potential benefits for each type of contribution and identify factors that could facilitate industry adoption of HRI research. However, we also recognize that there are several challenges associated with transferability, such as the diversity of well-defined job/skill-sets required from HRI practitioners, the lack of industry-led research, and the lack of standardization in HRI research methods. We discuss these challenges and propose potential solutions to bridge the gap between industry expectations and academic research in HRI.

Critical voices within and beyond the scientific community have pointed to a grave matter of concern regarding who is included in research and who is not. Subsequent investigations have revealed an extensive form of sampling bias across a broad range of disciplines that conduct human subjects research called "WEIRD": Western, Educated, Industrial, Rich, and Democratic. Recent work has indicated that this pattern exists within human-computer interaction (HCI) research, as well. How then does human-robot interaction (HRI) fare? And could there be other patterns of sampling bias at play, perhaps those especially relevant to this field of study? We conducted a systematic review of the premier ACM/IEEE International Conference on Human-Robot Interaction (2006-2022) to discover whether and how WEIRD HRI research is. Importantly, we expanded our purview to other factors of representation highlighted by critical work on inclusion and intersectionality as potentially underreported, overlooked, and even marginalized factors of human diversity. Findings from 827 studies across 749 papers confirm that participants in HRI research also tend to be drawn from WEIRD populations. Moreover, we find evidence of limited, obscured, and possible misrepresentation in participant sampling and reporting along key axes of diversity: sex and gender, race and ethnicity, age, sexuality and family configuration, disability, body type, ideology, and domain expertise. We discuss methodological and ethical implications for recruitment, analysis, and reporting, as well as the significance for HRI as a base of knowledge.

Human-Robot Interaction (HRI) is a highly multidisciplinary endeavor. However, it often still appears to be an effort driven primarily by technical aims and concerns. We outline some of the major challenges for fruitful interdisciplinary collaboration in HRI, arguing for an improved integration of psychology and applied social sciences and their genuine research agendas. Based on our own disciplinary backgrounds, we discuss these issues from vantage points mostly originating in applied engineering and psychology, but also from relevant related fields such as sociology, communication sciences, philosophy, arts, and design. We take a project-case as an example to discuss grounded and practical challenges in HRI research, and to propose how a combination of artificial intelligence advances and a better conceptual definition of the role of social sciences in HRI research may prove to be beneficial. Our goal is to strengthen the impact and effectiveness of social scientists working in HRI, and thereby better prepare the field for future challenges.

It's easy for us to forget that the vast majority of the world doesn't really care about (or even know about) robots. With that in mind, it's understandable why most roboticists consider robots operating "in the wild" to be "anywhere that isn't the controlled environment of my lab." But there are "real world" environments, and then there's the actual wild, and we almost never hear about research happening there. This is too bad, because we don't have nearly enough appreciation for how robots can potentially be used to mitigate problems throughout the developing world. There's also very little research into how different cultures react to robots with a social component--most human-robot interaction (HRI) studies rely on local participants who are easy (and cheap) to recruit, and are consequently full of students, which is a terrible representation of most of the rest of the world.

To enable a wide range of HRI research, a robust low-cost mobile base with a large sensor suite is essential. Neato robots provide such an alternative: they present a robust mobile platform with spring wheel suspension able to negotiate uneven surfaces, an impressive sensor suite including a LIDAR, a payload capability to host additional sensors and/or other light-weight robot platforms, such as a robot head or a manipulator, and safe, aesthetic design, suitable for interaction with a wide range of people in diverse, real-life environments.