Choosing the “Perfect” Scale: A Primer to Evaluate Existing Scales in HRI

This interdisciplinary workshop aims to break boundaries between the researchers who develop human models (e.g., from the fields of human factors, cognitive psychology, and computational neuroscience) and roboticists who use human models in different human-robot interaction (HRI) contexts. The keynote talks, contributed submissions, and interactive discussions will focus on the questions such as: How can modeling humans help us understand and design human-robot interactions? What kinds of models are useful for which HRI contexts (physical/cognitive interactions) and purposes (behavior prediction/personalization/theory -of- mind/etc.)? What common lessons can be learned from human behavior modeling in HRI across different application domains? How can modeling humans in HRI tasks help us to better understand human cognition/behavior? By stimulating an interdisciplinary conver-sation around these questions, we aim to raise awareness of the benefits of modeling and expose the wider HRI community to a variety of different modeling approaches, and facilitate the HRI researchers who already engage in modeling to exchange views on methodology of modeling and best nractices from diverse fields.

Motion detection is foundation of service robot vision in Human-Robot Interaction (HRI). Foreground detection is an important step of the motion detection. If the foreground detected is not correct, the motion detected will be wrong. To improve the efficiency and robustness of motion detection for HRI, a motion detection system for HRI based on image parameters and HRI context (MDIPC system) is proposed, which composes image pretreatment, image evaluation, foreground detection, foreground evaluation, and motion detection. At first, the framework of the MDIPC system is introduced. Secondly, evaluation of the Image Mean Filter in HLS color space is proposed. And thirdly, parameters representing the image feature and foreground in the HRI interaction are introduced, and these parameters include changing features of image's edge and the foreground objects' size. Experiments of typical interaction scenes validate that the proposed method provides much improved results. Quantitative evaluation shows that the proposed method is suitable for motion detection in HRI.

SummarySocial robots that can interact and communicate with people are growing in popularity for use at home and in customer-service, education, and healthcare settings. Although growing evidence suggests that co-operative and emotionally aligned social robots could benefit users across the lifespan, controversy continues about the ethical implications of these devices and their potential harms. In this perspective, we explore this balance between benefit and risk through the lens of human-robot relationships. We review the definitions and purposes of social robots, explore their philosophical and psychological status, and relate research on human-human and human-animal relationships to the emerging literature on human-robot relationships. Advocating a relational rather than essentialist view, we consider the balance of benefits and harms that can arise from different types of relationship with social robots and conclude by considering the role of researchers in understanding the ethical and societal impacts of social robotics.

A critical construct related to human-robot interaction (HRI) is autonomy, which varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots may interact with one another. Thus, there is a need to understand HRI by identifying variables that influence - and are influenced by - robot autonomy. Our overarching goal is to develop a framework for levels of robot autonomy in HRI. To reach this goal, the framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, the framework proposes a process for determining a robot's autonomy level, by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as guidelines to determine autonomy, categorize the LORA along a qualitative taxonomy, and consider which HRI variables (e.g., acceptance, situation awareness, reliability) may be influenced by the LORA.

The one-on-one human–robot interaction has expanded to the group level; robot groups are increasingly exerting psychosocial implications on human beings. However, how people interact with robot groups, especially how human factors and robot group factors coordinate to influence people’s responses to robot groups, is underexplored. To investigate this issue, the present study examined the interaction effect between individual differences in fixed and growth mindsets about the human mind and the fundamental characteristics of robot groups (i.e. entitativity) on responses to the robots during human–robot interaction. We induced mindsets (fixed or growth) about the human mind and manipulated the level of robot group entitativity (high or low) to capture responses to robots during human–robot interaction using virtual reality (VR) technology. The results revealed that a growth (versus fixed) mindset about the human mind promoted self-disclosure toward, and reduced behavioural anxiety with respect to robot groups with high (versus low) entitativity. We found that increased psychological closeness with robots accounted for these effects. Our findings contribute to research on the determinants of human–robot relationships and present implications for human–robot interactions at the group level.

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.

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.

“It's not Paul, it's a robot”: The impact of linguistic framing and the evolution of trust and distrust in a collaborative robot during a human-robot interaction

As the use of humanoid robots proliferates, an increasing amount of people may find themselves face-to-“face” with a robot in everyday life. Although there is a plethora of information available on facial social cues and how we interpret them in the field of human-human social interaction, we cannot assume that these findings flawlessly transfer to human-robot interaction. Therefore, more research on facial cues in human-robot interaction is required. This study investigated deception in human-robot interaction context, focusing on the effect that eye contact with a robot has on honesty toward this robot. In an iterative task, participants could assist a humanoid robot by providing it with correct information, or potentially secure a reward for themselves by providing it with incorrect information. Results show that participants are increasingly honest after the robot establishes eye contact with them, but only if this is in response to deceptive behavior. Behavior is not influenced by the establishment of eye contact if the participant is actively engaging in honest behavior. These findings support the notion that humanoid robots can be perceived as, and treated like, social agents, since the herein described effect mirrors one present in human-human social interaction.

Customization has been widely studied in the context of information systems and interfaces, but research on customization in human-robot interaction (HRI) is scarce. However, customization and user involvement may exert positive effects regarding attitudes and trust toward robots, hence improving HRI quality. The aim of the present work is to contribute to the theoretical understanding of customization in the HRI context by testing whether customization (none, low, or high) of a robot would elicit feelings of psychological ownership (PO), which, in turn, would increase trust toward the robot. Moreover, we hypothesized that the more people customize a robot, the less they would tend to anthropomorphize it. In line with our predictions, customization (vs. none) significantly increased psychological ownership and trust toward the robot. Further, the level of customization affected perceptions of robot agency. Additionally, PO mediated the effect of customization on trust toward the robot. The implications of these findings for research on HRI are discussed.KeywordsCustomizationSocial roboticsTrust toward robotsPsychological ownershipAnthropomorphism

Do I still like myself? Human-robot collaboration entails emotional consequences

Behavioral design of robot is one of the concerns in the human-robot interaction [1, 2]. About the design of human-robot communicative interaction, there are lots of approaches have been presented for finding the preferable behaviors that are accepted by the people. In these studies, the users impressions of robots during interactions with them have been focused on the initiatives of the users, with users evaluating the response of the robot. Conversely, there have less studies on the evaluations on human impressions when a robot takes the initiative and performs active behavior towards a human. While creating events in which a robot explicitly performed active behavior, we reviewed human-robot interactions and presented our behavioral designs. Based on that, we implemented greeting functions for the robot. The objective of this study is to investigate the users’ impressions on the robot especially with the activeness of the robot. We examined the differences in their impressions depending on with or without of active behavior of robot. The results show significant differences in activity, affinity, and intentionality.

Creativity, in one sense, can be seen as an effort or action to bring novelty. Following this, we explore how a robot can be creative by bringing novelty in a human–robot interaction (HRI) scenario. Studies suggest that proactivity is closely linked with creativity. Proactivity can be defined as acting or interacting by anticipating future needs or actions. This study aims to explore the effect of proactive behavior and the relation of such behaviors to the two aspects of creativity: 1) the perceived creativity observed by the user in the robot’s proactive behavior and 2) creativity of the user by assessing how creativity in HRI can be shaped or influenced by proactivity. We do so by conducting an experimental study, where the robot tries to support the user on the completion of the task regardless of the end result being novel or not and does so by exhibiting anticipatory proactive behaviors. In our study, the robot instantiates a set of verbal communications as proactive robot behavior. To our knowledge, the study is among the first to establish and investigate the relationship between creativity and proactivity in the HRI context, based on user studies. The initial results have indicated a relationship between observed proactivity, creativity, and task achievement. It also provides valuable pointers for further investigation in this domain.

Robot’s perception is essential for performing high-level tasks such as understanding, learning, and in general, human–robot interaction (HRI). For this reason, different perception systems have been proposed for different robotic platforms in order to detect high-level features such as facial expressions and body gestures. However, due to the variety of robotics software architectures and hardware platforms, these highly customized solutions are hardly interchangeable and adaptable to different HRI contexts. In addition, most of the developed systems have one issue in common: they detect features without awareness of the real-world contexts (e.g., detection of environmental sound assuming that it belongs to a person who is speaking, or treating a face printed on a sheet of paper as belonging to a real subject). This paper presents a novel social perception system (SPS) that has been designed to address the previous issues. SPS is an out-of-the-box system that can be integrated into different robotic platforms irrespective of hardware and software specifications. SPS detects, tracks, and delivers in real-time to robots, a wide range of human- and environment- relevant features with the awareness of their real-world contexts. We tested SPS in a typical scenario of HRI for the following purposes: to demonstrate the system capability in detecting several high-level perceptual features as well as to test the system capability to be integrated into different robotics platforms. Results show the promising capability of the system in perceiving real world in different social robotics platforms, as tested in two humanoid robots, i.e., FACE and ZENO.

The Technical Committee (TC) on Human - Robot Interaction (HRI) is 12 years old. The next ICRA in St. Paul, Minnesota, United States, on 14?18 May 2012 will be the time for the TC triennial review. We propose that notwithstanding the many results achieved during the past years, the TC in HRI will continue to play an important role in coordinating activities related to HRI, consolidating HRI communities, and pushing research toward new and underexplored areas. We envision several possible directions for future research: 1) new research for developing smart and natural human?robot interfaces 2) increasing the interdisciplinary nature of research in HRI by enlarging the HRI community, especially by linking and coordinating activities with sister groups such as human?computer interaction or human?machine interaction 3) investigating further the social impact of HRI 4) promoting field tests on HRI 5) new knowledge on performance evaluation and benchmarking. We invite all people with an interest in HRI to send us their ideas about future directions, activities, and proposals for renewing this TC.