Field Of Human-robot Interaction Research Articles

RISE: an open-source architecture for interdisciplinary and reproducible human-robot interaction research.

In this article, we present RISE-a Robotics Integration and Scenario-Management Extensible-Architecture-for designing human-robot dialogs and conducting Human-Robot Interaction (HRI) studies. In current HRI research, interdisciplinarity in the creation and implementation of interaction studies is becoming increasingly important. In addition, there is a lack of reproducibility of the research results. With the presented open-source architecture, we aim to address these two topics. Therefore, we discuss the advantages and disadvantages of various existing tools from different sub-fields within robotics. Requirements for an architecture can be derived from this overview of the literature, which 1) supports interdisciplinary research, 2) allows reproducibility of the research, and 3) is accessible to other researchers in the field of HRI. With our architecture, we tackle these requirements by providing a Graphical User Interface which explains the robot behavior and allows introspection into the current state of the dialog. Additionally, it offers controlling possibilities to easily conduct Wizard of Oz studies. To achieve transparency, the dialog is modeled explicitly, and the robot behavior can be configured. Furthermore, the modular architecture offers an interface for external features and sensors and is expandable to new robots and modalities.

The Key Strategies for Increasing Users’ Intention of Self-disclosure in Human-robot Interaction through Robotic Appearance Design

Bridging the communication gap between humans and social robots constitutes a critical area of research in the field of human-robot interaction (HRI). However, inducing self-disclosure in HRI is a challenging task, primarily due to the unique characteristics of social robots. This study aims to address the following research question: Can the design expression dimensions that influence user disclosure depth in the HRI process be deconstructed? Do design strategies take precedence over appearance design? Firstly, we segmented the focus of appearance design into three dimensions: Artistic Expression, Featured Expression, and Identified Expression, hypothesizing their level of significance. Subsequently, we created a prototype with balanced degrees for experimentation and invited 52 users to participate in HRI, guiding them to self-disclose and record data. Bayesian statistics were utilized to model the data and obtain comprehensive user feedback regarding appearance design. Based on the experimental results, we reviewed the content and potential order of the three dimensions and proposed our design and research model. Currently, at the theoretical level, there is no significant emphasis on users’ willingness to self-disclose in HRI. Our proposed model serves as a valuable tool for researchers in the fields of human-computer interaction, communication, and user experience design to gain quick insights into this topic. At the practical level, our model has reference significance for industrial and visual designers, enabling them to better comprehend the design goals, situations, and output design methods.

A New Knowledge Primitive of Digits Recognition for NAO Robot Using MNIST Dataset and CNN Algorithm for Children’s Visual Learning Enhancement

Aim/Purpose: Our study is focused on prototyping, development, testing, and deployment of a new knowledge primitive for the humanoid robot assistant NAO, in order to enhance student visual learning by establishing a human-robot interaction. Background: This new primitive, utilizing a convolutional neural network (CNN), enables real-time recognition of handwritten digits captured by the NAO robot, a humanoid robot assistant developed by SoftBank Robotics. It is equipped with advanced capabilities, including a wide range of sensors, cameras, and interactive features. By integrating the proposed primitive, the NAO robot gains the ability to accurately recognize handwritten digits, contributing to improved student visual learning experiences. Methodology: Our developed primitive consists of the use of a convolutional neural network (CNN) so that the robot is able to recognize the handwriting of the digits present in the input image received in real-time. The NAO robot establishes interaction with the learners through a scenario based on a predefined assignment. In this scenario, NAO captures the digit handwritten by the learner via its camera, recognizes the digit using the deep learning model generated by the MNIST dataset, and announces to the learner the handwritten digit in the input image. The prototype is realized using the concept of a distributed system allowing the distribution of tasks in four different computing nodes. Contribution: Our research makes a significant contribution by equipping the humanoid robot NAO with a cognitive intelligence system through the integration of a new knowledge primitive based on handwriting digit recognition (HWDR). Our approach used to create and implement this primitive in the NAO robot is interesting and innovative, and presents a promising provision for enhancing the visual learning experience of children and young students with special needs, based on the use of distributed systems that divide the work using various components distributed over several nodes, coordinating their efforts to perform tasks more efficiently than a single device besides the NAO robot. Findings: We designed our model using specific parameters and a fully convolutional neural network architecture, which includes three residual depthwise separable convolutions, each followed by batch normalization and ReLU activation. To evaluate the performance of our model, we tested it on the MNIST dataset, where we achieved a remarkable accuracy, F1 score, and recall of 99%. An experiment was conducted to test our implemented primitive and see the effectiveness of this invention for enhancing visual learning in children with special needs. We developed a visual learning strategy based on the creation of engaging activities mediated by the NAO robot in an educational context. The results showed that participants achieved a strong commitment to the NAO robot, appreciating its ability to recognize handwritten digits and highlighting its promising potential to enrich visual learning experiences. Participants expressed a strong preference for teaching methods integrating assistive learning technologies, demonstrating the positive impact of our humanoid assistant robot on improving learning and visual intelligence in an educational environment. Recommendations for Practitioners: Encourage creativity and innovation in the field of robotics and special needs. This can lead to new and effective solutions that improve the lives of students with special needs. Recommendation for Researchers: Test and evaluate the proposed robotics solutions to ensure they are effective and making a positive impact. Use feedback from users, educators, and parents to refine and improve your solutions. Also, ensure that the robotics solutions are accessible to students with a range of abilities. This may involve designing solutions that are adjustable or providing alternative means of access. Impact on Society: As there are several ways to educate, there are multiple forms of learning. With the help of this learning procedure and strategy, the human teacher collaborates with the robot assistance NAO to improve visual learning among students. The findings of this research can serve as an application for the implementation of various pedagogical methods that will assist in meeting the needs of the majority of learners. Future Research: Our future research will concentrate on addressing the educational needs of students with special needs, enabling them to overcome their challenges and reach academic excellence in an inclusive environment. To achieve this goal, we plan to leverage the capabilities of social robots, which have emerged as a significant contributor to the field of human-robot interaction, particularly in facilitating inclusive education. These agents have proven to be effective in providing support to students with special needs, thereby enabling them to receive the education they need to succeed.

The effects of robot's social praise on human responses in 2D game

Human-Robot Interaction (HRI) is an interaction between humans and robots through verbal and/or non-verbal cues. Studies in the HRI field concern the psychological effect, especially reactance, experienced by humans in decision-making situations with robots. This study applied persuasive attempts to investigate the impact of social praise by a social robot on human psychological reactance in a decision-making situation. The robot socially expressed its recognition (an acknowledgement of the existence) and reassurance (the action of removing someone's doubts) towards humans in a game, namely "Survival in Island". Social praise was expressed verbally in adoring human actions, such as 'Good Job' for following the robot's suggestions in making decisions. In the study, the participants were randomly divided into two conditions (social praise: presence vs absence) in a between-subject design study. Besides using questionnaires to measure humans' psychological conditions, the level of stress experienced by humans was also collected using a Galvanic Skin Response (GSR) sensor. The results indicated significant effects of social praise on perceived ease of use toward the robot, perceived intention to use the robot again in the future, perceived belief towards the robot and perceived compliance with the robot's suggestions through MANOVA tests. Other significant psychological and physiological effects of praise were not found in the study.

Preliminary psychometric scale development using the mixed methods Delphi technique

This study implemented a Delphi Method; a systematic technique which relies on a panel of experts to achieve consensus, to evaluate which questionnaire items would be the most relevant for developing a new Propensity to Trust scale. Following an initial research team moderation phase, two surveys were administered to academic lecturers, professors and Ph.D. candidates specialising in the fields of either individual differences, human-robot interaction, or occupational psychology. Results from 28 experts produced 33 final questionnaire items that were deemed relevant for evaluating trust. We discuss the importance of content validity when implementing scales, while emphasising the need for more documented scale development processes in psychology. Furthermore, we propose that the Delphi technique could be utilised as an effective and economical method for achieving content validity, while also providing greater scale creation transparency.

A neurorobotics approach to behaviour selection based on human activity recognition.

Behaviour selection has been an active research topic for robotics, in particular in the field of human-robot interaction. For a robot to interact autonomously and effectively with humans, the coupling between techniques for human activity recognition and robot behaviour selection is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting an initial neurorobotics model that embeds, in a simulated robot, computational models of parts of the mammalian brain that resembles neurophysiological aspects of the basal ganglia-thalamus-cortex (BG-T-C) circuit, coupled with human activity recognition techniques. A robotics simulation environment was developed for assessing the model, where a mobile robot accomplished tasks by using behaviour selection in accordance with the activity being performed by the inhabitant of an intelligent home. Initial results revealed that the initial neurorobotics model is advantageous, especially considering the coupling between the most accurate activity recognition approaches and the computational models of more complex animals.

Comparing the Performance and Evaluation of Computer Experts and Farmers when Operating Agricultural Robots: A Case of Tangible vs Mouse-Based UIs

Tangible user interfaces (TUIs) integrate computer systems with the physical world as they allow interaction through physical objects and the human kinesthetic system. They have been studied in various domains, both by the designing and piloting of innovative applications, as well as by comparatively analyzing TUI against GUI interaction. In the human-robot interaction (HRI) field, TUIs are considered a promising approach as they make effective use of physical object affordances, but research is rather inconclusive about whether TUIs offer improved efficiency, lower error rates, increased intuitiveness, engagement, and user satisfaction. In this study, two prototype UIs were designed and evaluated to compare a TUI with a 2D mouse-based UI for remotely operating two agricultural robots used for vineyard spraying. Two different user groups played the role of the operator: computer experts and farmers leading to an experiment with a 2×2 setup, where two different types of UIs were evaluated by two different user groups. The formulated research questions concern the efficiency, accuracy, and user evaluation for each UI and for each group individually and combined. Analysis has shown that there were no statistically significant differences comparing the alternative UIs for each group in terms of time to complete the task, even though computers experts were faster, as expected. Also, the number of collisions, as well as the percentage of unsprayed and double sprayed area, revealed no significant differences, either for user groups or UIs. The TUI received more positive evaluations in terms of user preference, and users reported lower perceived error rates, especially in the case of farmers, who were also more willing to use the TUI in their daily job.

A survey on the design and evolution of social robots — Past, present and future

Despite the relatively young age of Human–Robot Interaction (HRI) as a field, there is a large volume of research on advances in robot hardware, software and behavior. The goal of this article is to survey trends in social robot design, to provide an evidence-based approach and guidelines that can inform future social robot development. To this end, this article systematically reviews the evolution of social robots with a focus on their applications, technical features and design. In total 9920 articles from ACM Digital Library (n=4223) and IEEE Explore (n=5697) were reviewed. In order to make this review as inclusive as possible, a broad definition of social robots was used to make decisions about inclusion/exclusion of a given social robot during the review process. As a result, a total of 344 social robots were examined in the review with features being embodiment, mobility, total number of degrees of freedom, existence of a manipulator, size, weight, shell build, applications, target user group, commercial availability, social software capabilities, sensors, interaction modalities, face, software extension capability and initial release year. This resulted in a rich dataset with detailed information about the social robots used in the HRI field. We also provide design guidelines for social robots to inform future research. Findings of this review may help both researchers & practitioners to select, and/or design, the best social robot for their particular experiment or application scenario.

Findings From A Qualitative Field Study with An Autonomous Robot in Public: Exploration of User Reactions and Conflicts

Soon service robots will be employed in public spaces with frequent human-robot interaction (HRI). To achieve a safe, trustworthy and acceptable HRI, service robots need to be equipped with interaction strategies suitable for the robot, user, and context. To gain realistic insights into the initial user reactions and challenges that arise when a mechanoid, autonomous service robot in public is applied, a field study with three data sources was conducted. In a first step, lay users’ intuitive reactions to a cleaning robot at a train station were observed (N = 344). Second, passersby’s preferences for HRI interaction strategies were explored in interviews (n = 54). As a third step, trust and acceptance of the robot were assessed with questionnaires (n = 32). Identified challenges were social robot navigation in crowded places also applicable to vulnerable passersby, inclusive communication modalities, information of staff and public about the service robot application and the need for conflict resolution strategies to avoid an inefficient robot (e.g., testing behavior, path is blocked). This study provides insights into naive HRI in public and illustrates challenges, provides recommendations supported by literature and highlights aspects for future research to inspire a research agenda in the field of public HRI.

Android as a Receptionist in a Shopping Mall Using Inverse Reinforcement Learning

For human-robot interaction (HRI), it is difficult to hand-craft all the rules for robots owing to diverse situations. Therefore, inverse reinforcement learning (IRL) is a potential solution that helps transfer human knowledge about interactions to robots. However, the feasibility of practically using IRL for HRI remains unknown. Here, we demonstrate a practical HRI application of IRL. An android was trained using IRL and acted as a receptionist to encourage visitors to practice hand hygiene in a shopping mall. We found that android learning through IRL has a competitive ability to a well-trained human operator on the reception task. Furthermore, we found that the android maintained high performance regardless of customer traffic. Our results demonstrate the potential of IRL in advancing the social HRI field. We anticipate that our work will be a starting point for using IRL in future HRI applications.

Comment on Paolo Euron’s “Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots”

Paolo Euron's "Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots" lays out a vision for appreciating sex robots in aesthetic terms, centering the concept of "beauty" as a measure of what they can inspire culturally and existentially. In these comments I turn toward the field of human-robot interaction and the ethical challenges that inhabit the core of such an aesthetic turn.

Connecting the Dots of Social Robot Design From Interviews With Robot Creators.

Despite promises about the near-term potential of social robots to share our daily lives, they remain unable to form autonomous, lasting, and engaging relationships with humans. Many companies are deploying social robots into the consumer and commercial market; however, both the companies and their products are relatively short lived for many reasons. For example, current social robots succeed in interacting with humans only within controlled environments, such as research labs, and for short time periods since longer interactions tend to provoke user disengagement. We interviewed 13 roboticists from robot manufacturing companies and research labs to delve deeper into the design process for social robots and unearth the many challenges robot creators face. Our research questions were: 1) What are the different design processes for creating social robots? 2) How are users involved in the design of social robots? 3) How are teams of robot creators constituted? Our qualitative investigation showed that varied design practices are applied when creating social robots but no consensus exists about an optimal or standard one. Results revealed that users have different degrees of involvement in the robot creation process, from no involvement to being a central part of robot development. Results also uncovered the need for multidisciplinary and international teams to work together to create robots. Drawing upon these insights, we identified implications for the field of Human-Robot Interaction that can shape the creation of best practices for social robot design.

Novelty Knows No Boundaries: Why a Proper Investigation of Novelty Effects Within SHRI Should Begin by Addressing the Scientific Plurality of the Field.

Research on psychological novelty effects within the fields of Social Robotics and Human-Robot Interaction (together: SHRI) so far has failed to gather the momentum it deserves. With the aid of exemplary descriptions of how psychological novelty is currently approached and researched across (certain main regions of) the larger scientific landscape, I argue that the treatment of novelty effects within the multidisciplinary SHRI reflects larger circumstances of fragmentation and heterogeneity in novelty research in general. I further propose that while the concept of novelty may currently function as a Boundary Object between the contributing domains of SHRI, a properly integrated, interdisciplinary concept of novelty is needed in order to capture and investigate the scope and scale of novelty effects within research on social human-robot interaction. Building on research on the New Ontological Category Hypothesis and related studies, I argue that the novelty of social robots can be understood as radical to the extent that their comprehension requires revisions of traditional core categories of being. In order to investigate the sui generis effects of such novelty, which should not be narrowly understood as mere “noise” in the data, it is paramount that the field of SHRI begin by working out a shared, integrative framework of psychological novelty and novelty effects.

Multifunctional Tactile Feedbacks Towards Compliant Robot Manipulations via 3D-Shaped Electronic Skin

Electronic skin (e-skin) has attracted considerable interests to endow robots the abundant perception abilities like human skin. However, multifunctional tactile feedbacks and poor wearability hinder the practical application towards compliant robot manipulations and friendly human-robot interaction (HRI). Here, a 3D-shaped e-skin integrated with hierarchically distributed flexible pressure, proximity, humidity sensors, and digital conversion circuits has been developed via the parting casting method. It can perfectly cover the non-developable and time-dynamic curved surface of a bionic hand and shows excellent wearability. The sensing data are collected by multichannel acquisition circuits and then sent to processing center to establish the mapping relations between multiple stimuli and movements of the bionic hand and arm. The multifunctional tactile feedbacks have been successfully demonstrated, which include non-contact obstacle avoidance, grasping force control, slip detection, and voice feedback. The e-skin shows great potential in the fields of HRI, artificial limbs, health care robots, and service robots.

Design and Stiffness Analysis of a Novel 7-DOF Cable-Driven Manipulator

The lightweight design of robots is an important factor in the field of human-robot interaction. Thus, a lightweight 7-DOF cable-driven manipulator is proposed and manufactured, including a shoulder joint with 3-DOF, an elbow joint with 1-DOF, and a wrist joint with 3-DOF. The offset design of the shoulder joint increases the workspace and facilitates the electrical wiring of the manipulator. To solve the cable relaxation problem of the long-term operation of the manipulator, novel cable tensioning mechanisms are proposed based on the characteristics of shoulder, elbow, and wrist joints, which enable rapid tensioning during the motion process. Moreover, forward/inverse kinematic model are established based on virtual coupling joints to analysis the performance of the manipulator. Both the tensile tests of the cables and the stiffness experiments of the wrist and elbow joints are carried out The experimental results show that the maximum stiffness values of the elbow joint and the wrist joint are 1066.3 N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula> m/rad and 319.8 N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula> m/rad respectively, which can be comparable to the human arm.

Have I Got the Power? Analysing and Reporting Statistical Power in HRI

This article presents a discussion of the importance of power analyses, providing an overview of when power analyses should be run in the context of the field of Human-Robot Interaction, as well as some examples of how to perform a power analysis. This work was motivated by the observation that the majority of papers published in the proceedings of recent HRI conferences did not report conducting a power analysis; an observation that has concerning implications for many conclusions drawn by these studies. This work is intended to raise awareness and encourage researchers to conduct power analyses when designing research studies using human participants.

Lessons Learned About Designing and Conducting Studies From HRI Experts.

The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees’ feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants’ responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot’s limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research.

Human-Like Robots and the Uncanny Valley

Abstract. In the field of human-robot interaction, the well-known uncanny valley hypothesis proposes a curvilinear relationship between a robot’s degree of human likeness and the observers’ responses to the robot. While low to medium human likeness should be associated with increased positive responses, a shift to negative responses is expected for highly anthropomorphic robots. As empirical findings on the uncanny valley hypothesis are inconclusive, we conducted a random-effects meta-analysis of 49 studies (total N = 3,556) that reported 131 evaluations of robots based on the Godspeed scales for anthropomorphism (i.e., human likeness) and likeability. Our results confirm more positive responses for more human-like robots at low to medium anthropomorphism, with moving robots rated as more human-like but not necessarily more likable than static ones. However, because highly anthropomorphic robots were sparsely utilized in previous studies, no conclusions regarding proposed adverse effects at higher levels of human likeness can be made at this stage.

1D Convolutional Autoencoder-Based PPG and GSR Signals for Real-Time Emotion Classification

To apply emotion recognition and classification technology to the field of human-robot interaction, it is necessary to implement fast data processing and model weight reduction. This paper proposes a new photoplethysmogram (PPG) and galvanic skin response (GSR) signals-based labeling method using Asian multimodal data, a real-time emotion classification method, a 1d convolutional neural network autoencoder model, and a lightweight model obtained using knowledge distillation. In addition, the model performance was verified using the public DEAP dataset and the Asian multi-modal dataset &#x2019;MERTI-Apps&#x2019;. For emotion classification, bio-signal data were window-sliced in 1-pulse units, and the label was reset to reflect the characteristics of the PPG and GSR signals. Simple data pre-processing, such as the prevention of loss and waveform duplication, was performed without using handcrafted features. The experiment showed that the accuracy of the proposed model using MERTI-Apps was 79.18% and 74.84% in the case of arousal and valence, respectively, for 3-class criteria, and the accuracy of the proposed model using DEAP was 81.33% and 80.25% in the case of arousal and valence, respectively, for 2-class criteria. The accuracy of the lightweight model was 77.87% and 73.49% in the case of arousal and valence, respectively, for 3-class criteria and its calculation time was reduced by more than 80% compared to the proposed 1d convolutional autoencoder model. We also confirmed that the proposed model improved computational time and accuracy compared to previous studies using MERTI-Apps and the lightweight model used in limited hardware environments enabled fast computation and real-time emotion classification.

Human-robot interaction methodology: Robot teaching activity

Research on the use of social robots in education is constantly increasing in the growing field of human-robot interaction (HRI). Consequently, it is essential to determine an appropriate methodology to test how these robots can optimally interact with students. This study specifically looks at how we can use existing knowledge from psychology, neuroscience and educational research and apply them with validity and credibility in HRI studies. We are interested in incorporating research methodologies to evaluate the performance of social robots acting as university professors in a real classroom environment. Moreover, we aim to measure three effects, a) students’ knowledge acquisition (quiz after lecture and final exam grades), b) level of enjoyment (Likert scale questionnaire), and c) level of surprize (analysis of facial expressions filmed by cameras). To identify the relationship between students’ knowledge acquisition, enjoyment, and level of surprize, we designed a series of three experiments, testing three variables: 1. one human-tutor lesson, 2. one robot-tutor lesson, 3. two robot-tutor lessons. In this paper we thoroughly explain the methods used to measuring and testing these variables based on modern and reliable sources.•Application of Psychological Research Methods to Human-Robot Interaction Studies.