Human-robot Interaction Perspective Research Articles

Consumer acceptance of social robots in domestic settings: A human-robot interaction perspective

Consumer acceptance of social robots in retail and service settings is an emerging field in marketing research. Social Home Robots (e.g., Jibo, Cozmo, and Kuri) serving various groups in domestic settings show significant retail and consumer service potential. However, most current research focuses on social robots that serve older or disabled individuals in domestic settings, leaving a gap in understanding consumer acceptance of Social Home Robots, particularly across different cultures. From the Human-Robot Interaction perspective, this article examines consumers' intentions to use Social Home Robots through a sequential mixed-method approach. Our findings indicate that consumers' intentions to use these robots are primarily influenced by key factors in Human-Robot Interaction: belief in social robot benefits, concern about privacy risks, trust, the robots' overall anthropomorphic design, and consumers' self-construal tendency. Furthermore, trust mediates the relationships between belief in social support, privacy concerns, independent self-construal, and usage intention. This study explores consumer acceptance of social robots serving diverse groups rather than special groups in domestic settings, contributing to the literature on social/service robots. It also offers business guidelines for designing and marketing Social Home Robots in retail.

Comfortability Analysis Under a Human–Robot Interaction Perspective

Interactions entail a tangled mix of emotional states that emerge between the people who are communicating. Being capable of comprehending these states help us to adapt to our partner’s needs enhancing the interaction. In the same fashion, we believe that robots capable of such skills would be better integrated in society. Hence, this paper tackles the internal state that focuses on the unfolding of any social exchange: Comfortability. It explores whether a humanoid robot can have an impact on humans Comfortability and explores the way people might behave. To this aim, the iCub robot interviewed 29 non-HRI researchers for a real column of the IIT Opentalk online magazine. During the interview the robot complimented, ignored, interrupted, and insulted the participant with the intention of making them feel opposite Comfortability levels. The results concluded that a humanoid robot can affect people’s Comfortability highlighting that not everyone perceives, assimilates, and reacts to the same situations in the same way. The findings suggest that self-reports and Valence/Arousal estimations are not reliable measures to determine someone’s Comfortability and that external factors (e.g., attitude towards robots or the robot’s perception) might affect it. On top of that, a list of 28 visual features associated with low Comfortability levels is included, providing support to keep unraveling Comfortability in further studies.

Learning Dynamic Robot-to-Robot Object Handover

Object handover is an essential skill for collaborative robots in both services robotics and manufacturing scenarios. Most previous works were conducted from the perspective of human-robot interaction. The object handover between robots for collaborative task execution, aiming at optimizing time efficiency with human-like smooth behaviour, has not been extensively addressed. In this work, we propose a skill framework based on variable impedance control and dynamic motion primitives to optimize not only the motion trajectories and variable impedance, but also the timing of hand actions during dynamic motion. The effectiveness of the proposed framework is evaluated on a real dual-arm robot system under two handover scenarios with different constraints on the timing of hand actions. The experiment results demonstrate significant time efficiency improvement with reduction of the execution time by 9.4% and 23.7%, compared with accelerating the motion speed of the demonstrated handover. Furthermore, it can be observed that the robot successfully learned dynamic object handover without requiring transfer action to be triggered after both hands stop and remain still. In addition, in the second experiment, it is shown that the object can be transferred even without ensuring firm contact, which indicates that object handover is possible to be realized by throwing-like motion.

Baseball Fans’ Evaluations of Robot Umpire: The Perspective of Human-Robot Interaction

PURPOSE The current study examined how baseball fans evaluate a robot umpire from the perspective of the human-robot interaction. In particular, this study examined whether baseball fans evaluated more positively a robot umpire depending on whether a robot or a human umpire has the final authority to make ball-strike decisions, when the ball-strike decision contradicts with each other. Furthermore, the different types of expertise (baseball vs. image analysis) embedded in robot umpire and fans’ levels of technology adoption were used to examine the relationship between umpire type and evaluations of robot umpire. METHODS This study used 2 (final decision maker: robot vs. human umpire) by 2 (different types of expertise embedded in robot umpire: baseball vs. image analysis) by 2 (different levels of technology adoption: low vs. high) between-subjects design.RESULTS The results indicated that when a robot umpire had the final authority to make ball-strike decisions, baseball fans evaluated more positively the adoption of robot umpire, when image analysis expertise was embedded in the robot umpire rather than baseball expertise. Meanwhile, baseball fans evaluated more positively the adoption of robot umpire when baseball expertise was embedded in a robot umpire rather than image analysis expertise. CONCLUSIONS The current study provides meaningful implications regarding how to design an effective system for the operation of robot umpire in baseball.

Driven by technology or sociality? Use intention of service robots in hospitality from the human–robot interaction perspective

The technology and sociality of service robots in the hospitality industry are integrated into iTAM from the perspective of human–robot interactions. The empirical results of the functional-social perceived model show that both functional and social perceptions mediate the relationship between human–robot interaction and the intention to use robots, and that the mediating effect of technology is stronger than that of social perception. In terms of functional perception, the mediating effect of perceived usefulness was stronger than that of perceived ease of use. In terms of social perception, the mediating effect of perceived enjoyment was significant, while the mediating effect of rapport and trust was not. Therefore, customers' perceived enjoyment is stronger than that of rapport and trust.

A growth mindset about human minds promotes positive responses to intelligent technology

Perceiving minds in technology agents, for example, robots designed with artificial intelligence (AI), is common and crucial in modern life. However, past studies have revealed that robots with a high level of minds elicit polarized responses. From a human-robot interaction perspective, we proposed that people’ responses to robots, in part, originate from differences between fixed and growth mindsets about human minds—the beliefs regarding whether humans' mental capacities are fixed or incremental. We conducted five studies to test this assumption. A growth mindset about human minds was associated with or led to lower levels of negative feelings about robots (Study 1), more perceptions of robots as allies versus enemies (Study 2), more support for robotic research (Studies 3 and 4), and greater willingness to interact with robots (Study 5). Furthermore, the effect of a growth mindset about human minds on favorable responses to robots was more pronounced when robots were perceived as having high (versus low) levels of minds (Studies 3–5) and mediated by decreased concerns about robots (Study 5). By emphasizing the nuanced role of mindset beliefs about human minds in responses to intelligent technology, this research provides not only a new perspective on research into minds but also important implications for human-technology relationships.

Human-Robot Interaction in Rehabilitation and Assistance: a Review

Research in assistive and rehabilitation robotics is a growing, promising, and challenging field emerged due to various social and medical needs such as aging populations, neuromuscular, and musculoskeletal disorders. Such robots can be used in various day-to-day scenarios or to support motor functionality, training, and rehabilitation. This paper reflects on the human-robot interaction perspective in rehabilitation and assistive robotics and reports on current issues and developments in the field. The survey on the literature reveals that new efforts are put on utilizing machine learning approaches alongside novel developments in sensing technology to adapt the systems with user routines in terms of activities for assistive systems and exercises for rehabilitation devices to fit each user’s need and maximize their effectiveness. A review of recent research and development efforts on human-robot interaction in assistive and rehabilitation robotics is presented in this paper. First, different subdomains in assistive and rehabilitation robotic research are identified, and accordingly, a survey on the background and trends of such developments is provided.

A Human-Robot Interaction Perspective on Assistive and Rehabilitation Robotics.

Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human–robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions.

Methods for Evaluating and Comparing the Use of Haptic Feedback in Human-Robot Interaction with Ground-Based Mobile Robots

A significant amount of research has been conducted regarding the technical aspects of haptic feedback. However, the design of effective haptic feedback behaviors for controlling ground-based mobile robots is not yet well understood from a human-robot interaction perspective. Past research of haptic feedback behaviors for mobile robots has sometimes made use of control paradigms that do not appropriately map to teleoperation or supervision tasks. Furthermore, evaluation of haptic behaviors has not been systematic and often only demonstrates feasibility. As a result, comparing various techniques is difficult. In this article, we focus on how haptic control research could be improved in the domain of teleoperation and supervision of ground-based mobile robots through the introduction of a haptic evaluation toolkit.

Long‐term LEGO therapy with humanoid robot for children with ASD

AbstractTo utilise the knowledge gained from highly specialised domains as autism therapy to robot‐based interactive training platforms, an innovative design approach is needed. We present the process of content creation and co‐design of LEGO therapy for children with autism spectrum disorders performed by a humanoid robot. The co‐creation takes place across the disciplines of autism therapy, and behavioural robotics, and applies methods from design and human–robot interaction, in order to connect state‐of‐the‐art developments in these disciplines. We designed, carried out and analyzed a pilot and final experiment, in which a robot mediated LEGO therapy between pairs of children was mediated by a robot over the course of 10 to 12 sessions. The impact of the training on the children was then analysed from a clinical and human–robot interaction perspective. Our major findings are as follows: first, game‐based robot scenarios in which the game continues over the sessions opened possibilities for long‐term interventions using robots and led to a significant increase in social initiations during the intervention in natural settings; and second, including dyadic interactions between robot and child within triadic games with robots has positive effects on the children's engagement and on creating learning moments that comply with the chosen therapy framework.

Design of Sensing System and Anticipative Behavior for Human Following of Mobile Robots

The human-following behavior design for mobile robots is considered in this paper. In particular, the issue should be addressed from the perspective of human-robot interaction since humans are aware of the following actions. This makes the problem quite different from human tracking where recognition and location accuracy are the main concerns. An anticipative humanfollowing behavior is proposed by incorporating the human model. The human model is constructed using relevant scientific studies about human walk and social interaction, allowing the robot to predict the human trajectory and to take preemptive action. To realize the idea, it is necessary to have a robust sensing system that is capable of tracking the human location persistently. In this paper, we also propose a sensing system based on a novel 3-D meanshift algorithm on RGBD camera. The system performance is assessed through experimental evaluation of three specific humanfollowing scenarios: following from behind, following on the side, and following in front. Each of these scenarios has its particularities and applications, thus providing insight about the effectiveness and usability of anticipative behavior.

Humanoid Path Planning From HRI Perspective: A Scalable Approach via Waypoints With a Time Index

This paper proposes a path planner for a humanoid robot to enhance its performance in terms of the human-robot interaction perspective. From the human point of view, the proposed method uses the time index that can generate a path that humans feel to be natural. In terms of the robot, the proposed method yields a waypoint-based path, the simplicity of which enables accurate tracking even for humanoid robots with complex dynamics. From an environmental perspective through which interactions occur, the proposed method can be easily expanded to a wide area. Overall, the proposed method can be described as a scalable path planner via waypoints with a time index for humanoid robots. Experiments have been conducted in test beds where the robot encounters unexpected exceptional situations. Throughout these trials, the robot successfully reached the goal location while iteratively replanning the path.

Keyframe-based Learning from Demonstration

We present a framework for learning skills from novel types of demonstrations that have been shown to be desirable from a Human–Robot Interaction perspective. Our approach—Keyframe-based Learning from Demonstration (KLfD)—takes demonstrations that consist of keyframes; a sparse set of points in the state space that produces the intended skill when visited in sequence. The conventional type of trajectory demonstrations or a hybrid of the two are also handled by KLfD through a conversion to keyframes. Our method produces a skill model that consists of an ordered set of keyframe clusters, which we call Sequential Pose Distributions (SPD). The skill is reproduced by splining between clusters. We present results from two domains: mouse gestures in 2D and scooping, pouring and placing skills on a humanoid robot. KLfD has performance similar to existing LfD techniques when applied to conventional trajectory demonstrations. Additionally, we demonstrate that KLfD may be preferable when demonstration type is suited for the skill.

ONLINE TRANSFER OF HUMAN MOTION TO HUMANOIDS

Transferring motion from a human demonstrator to a humanoid robot is an important step toward developing robots that are easily programmable and that can replicate or learn from observed human motion. The so called motion retargeting problem has been well studied and several off-line solutions exist based on optimization approaches that rely on pre-recorded human motion data collected from a marker-based motion capture system. From the perspective of human robot interaction, there is a growing interest in online motion transfer, particularly without using markers. Such requirements have placed stringent demands on retargeting algorithms and limited the potential use of off-line and pre-recorded methods. To address these limitations, we present an online task space control theoretic retargeting formulation to generate robot joint motions that adhere to the robot's joint limit constraints, joint velocity constraints and self-collision constraints. The inputs to the proposed method include low dimensional normalized human motion descriptors, detected and tracked using a vision based key-point detection and tracking algorithm. The proposed vision algorithm does not rely on markers placed on anatomical landmarks, nor does it require special instrumentation or calibration. The current implementation requires a depth image sequence, which is collected from a single time of flight imaging device. The feasibility of the proposed approach is shown by means of online experimental results on the Honda humanoid robot — ASIMO.

Social Interactions in HRI: The Robot View

This paper explores the topic of human-robot interaction (HRI) from the perspective of designing sociable autonomous robots-robots designed to interact with people in a human-like way. There are a growing number of applications for robots that people can engage as capable creatures or as partners rather than tools, yet little is understood about how to best design robots that interact with people in this way. The related field of human-computer interaction (HCI) offers important insights, however autonomous robots are a very different technology from desktop computers. In this paper, we look at the field of HRI from an HCI perspective, pointing out important similarities yet significant differences that may ultimately make HRI a distinct area of inquiry. One outcome of this discussion is that it is important to view the design and evaluation problem from the robot's perspective as well as that of the human. Taken as a whole, this paper provides a framework with which to design and evaluate sociable robots from a HRI perspective.