This research aims to demonstrate how integrating human-robot teaming dynamics into mission planning tools impacts the abilities of robot operators as they coordinate multiple robot agents during a mission. This was investigated in a pilot study using two inter-robot collaboration modalities and interface tools, which required different human-robot interaction techniques to execute a mission with a team of four robots. In the first modality, the operator manually inserted waypoints for each robot, as they acted as individual agents. In the second modality, the operator used the Planning Execution to After-Action Review (PETAAR) toolset to plot a single waypoint for the team of robots, as the robots coordinated their movement as a group. One novel component of this study is the investigation of how human-robot teaming dynamics and the PETAAR toolset impacted robot operators' real-time situation awareness and perceived cognitive load as well as team performance. Although the teaming modalities differed greatly with respect to the level of operator input needed, the time required to complete the simulation, the participant's perceived cognitive load, and interface usability were very similar for both modalities. In contrast, the results revealed statistically significant differences between the two teaming modalities related to participants' abilities to maintain a wedge formation while remaining situationally aware. Results from this work will be used to guide development of PETAAR along with the design of future studies investigating more complex teaming scenarios and for creating a baseline for comparing future results.

Socially assistive robots can be used as therapeutic technologies to address depression symptoms. Through three sets of workshops with individuals living with depression and clinicians, we developed design guidelines for a personalized therapeutic robot for adults living with depression. Building on the design of Therabot, workshop participants discussed various aspects of the robot's design, sensors, behaviors, and a robot connected mobile phone app. Similarities among participants and workshops included a preference for a soft textured exterior and natural colors and sounds. There were also differences - clinicians wanted the robot to be able to call for aid, while participants with depression differed in their degree of comfort in sharing data collected by the robot with clinicians.

Deciding where to go is one of the primary challenges in designing an agent that can explore an unknown environment. Grid-worlds provide a flexible framework for representing different variations of this problem, allowing for various types of goals and constraints. Typically, agents move one cell at a time, gathering new information at each time step. However, recomputing a new action after each step can lead to unintended behaviors, such as indecision and forgetting about previous goals. To mitigate this, we define a set of persistent feature layers that can be used by either a linear weighted policy or a neural network approach to identify potential destination locations. The outputs of these policies are processed using knowledge of the environment to ensure that objectives are met in a timely and effective manner. We demonstrate how to train and evaluate a U-Net model in a custom grid-world environment and provide guidance and suggestions for how to use this approach to build complex agent behaviors.

Using socially assistive robots (SARs) as specialized companions for those living with depression to manage symptoms provides a unique opportunity for exploration of robotic systems as comfort objects. Moreover, the robotic components allow for specialized behavioral responses to particular stimuli, as preferred by the user. We have conducted semi-structured interviews with 10 participants about the zoomorphic robot’s Therabot™ desired behaviors and focus groups with five additional participants regarding the preferred sensors within the Therabot™ system. In this paper, using the data from interviews and focus groups, we explore SAR input and output for depression management. While participants overall expected the robot to respond in much similar ways as a well-trained service animal, they expressed interest in the robot understanding unique information about the environment and the user, such as when the user might need interaction.

Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer’s, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people’s daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments? To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (United States and South Korea) with SARs deployed in each user’s home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and SAR. Models built on that data were capable of achieving nearly 84% accuracy for detecting specific interaction modalities (AUC 0.885) when trained/tested on the same location, though suffered significant performance drops when applied to a different location. Further analysis and participant interviews showed that was likely due to differences in home living environments in the US and Korea. The results suggest that our ability to create adaptable behaviors for robotic pets may be dependent on the human-robot interaction (HRI) data available for modeling.

Modern machine learning (ML) is based to a great extent on supervised deep learning models that require large amounts of labeled training data. While image data sets with annotations exist, the annotations are produced manually and possess relatively simple descriptions. To date, none of the freely available labeled image data sets incorporate spatial reasoning, one of Gardner's nine human intelligences. This article presents a new process with open source tools provided to label imagery based on spatial interactions between image objects and auto-mated reasoning under uncertainty. The resulting annotated data can be used to train new ML/AI algorithms and/or help us better understand existing methodologies.

While Wizard-of-Oz (WOz) techniques are frequently used to supplement a machine’s abilities, extending this approach to human entities can increase experimental control in studies comparing evaluations of humans and machines in the same role. This article describes the design, implementation, and use of a WOz system for facilitating controlled verbal interactions between children and robot or human interviewers. A collaborative interface allows multiple remote wizards to combine participant responses with interaction-specific goals in order to direct a robot or human interviewer’s behavior in a consistent manner. While robot interviewers are controlled directly, human interviewers receive direction through a tablet device or via a projection system concealed from participants. In addition to the system’s technical design, we describe the division of responsibilities between wizards and insights from using the system across three extensive interview studies to facilitate a total of 217 interactions with children.

Obstacle detection and avoidance plays a crucial role in autonomous navigation of unmanned ground vehicles. This becomes more challenging in off-road environments due to the higher probability of finding negative obstacles (e.g., holes, ditches, trenches, etc.) compared with on-road environments. One approach to solve this problem is to avoid the candidate path with a negative obstacle, but in off-road avoiding negative obstacles all the time is not possible. In such cases, the path planner may need to choose a candidate path with a negative obstacle that causes the least amount of damage to the vehicle. To deal better with these types of scenarios, this study introduces a novel approach to perform shape estimation of negative obstacles using LiDAR 3D point cloud data. The dimensions (width, diameter, and depth) and the location (center) of negative obstacles are calculated based on estimated shape. This approach is tested on different terrain types using the Mississippi Autonomous Vehicle Simulation (MAVS).

The objective of this research was to investigate how humans would respond to unmanned ground vehicles (UGV) operating in their environment. These environments included a shopping mall, two different sports complexes, and a university campus setting. The field study included video observations of 784 pedestrians. Additionally, survey data were collected and matched to 115 pedestrian observations. The field evaluations were conducted over a period of six weeks at the four locations. The results indicate that of the 784 pedestrians observed, 63.5% took time to observe the robot, 30.6% ignored the robot, and 5.9% stopped and interacted with the robot. Based on the individual results from the 115 pedestrians that completed the survey, those who indicated having positive feelings (e.g., strong, comfortable, excited) had an increased likelihood of interacting with the UGV. If the pedestrian had owned a dog they were more likely to interact with the robot. Pedestrians traveling in groups were also more likely to stop and interact with the robot than those traveling alone. The results from this field research will be used in the near future to inform the development of a pedestrian model to include responses to robots that may be in the environment, and could also be used to inform design features for UGVs. As robots continue to become more prevalent in society, it is important to consider how humans will interact with them when encountered.

After playing a five to seven minute character guessing game with a Nao robot, children answered questions about their perceptions of the robot's abilities. Responses from interactions with 30 children, ages eight to twelve, showed that when the robot made an attempt at guessing the participant's character, rather than being stumped and unable to guess, the robot was more likely to be perceived as being able to understand the participant's feelings and able to provide advice. Regardless of their game experience, boys were more likely than girls to feel they could have discussions with the robot about things they could not talk to other people about. This article provides details associated with the implementation of a game used to guess a character the children selected; a twelve question verbally-administered survey that examined their perceptions of the robot; quantitative and qualitative results from the study; and a discussion of the implications, limitations, and future directions of this research.

Technology is in a constant state of evolution, which allows for new and cunning cyber-attacks and tactics. Out of all these tactics, the exploitation of human cognitive biases in response to phishing attacks is challenging to defend against. The purpose of this study was to determine if humans could discriminate fraudulent Uniform Resource Locators (URLs) or links from legitimate URLs without the aid of specific hardware or software. We also explored whether simple textual manipulations were easier to detect compared to complex manipulations. Participants (N = 1044) completed the following: (1) A demographic questionnaire including their internet and email usage, (2) a role-playing exercise where participants were shown a series of emails from an inbox and had to select the action(s) that they would take, and (3) a series of questions related to technology and security to assess their prior knowledge and awareness of phishing. Results indicated that it was difficult for participants to correctly identify URLs when checking email. Results also revealed that difficulty in detecting simple textual manipulations versus complex manipulations was category dependent.

We are happy to present this Special Issue on Education in Human-Robot Interaction (HRI) to the community. As HRI has matured as a field, it is also becoming an increasingly popular educational topic and resource at all levels of instruction, from elementary through graduate programs. While several excellent review articles for the field exist, there is no textbook or recognized curriculum in HRI. The interdisciplinary nature of the field presents students and instructors with opportunities for building on diverse perspectives from design, engineering, computer science, and the social sciences and humanities, as well as challenges in presenting and working with material from such a broad array of disciplines. The authors in this special issue discuss their experiences with and strategies for designing HRI curricula and teaching HRI to students of diverse backgrounds and skill sets. We hope this special issue will inspire many more courses, summer schools, and educational outreach activities in HRI. We also hope that it sparks more discussions about diverse approaches to and necessary standards for HRI curricula.

Intelligent tutoring systems, electronic fitness bands, and assistive robots all contribute toward increasing the wellness of humans by providing different forms of support. As a class of machines focused on increasing the physical and psychological well-being of users emerges, new design considerations and questions arise. We are exploring the potential of prosocial machines via social robotic systems focused on enhancing a user's psychological well-being through positive interventions. This article briefly summarizes our initial approach to understanding prosocial machines via a positive psychology based intervention focused on comparing the ability of different systems to induce measurable increases in a human's level of hopefulness.

This article describes the results of a study that compares disclosure occurrences of bullying from children (ages 8 to 12) to either a human or a social robot. Results from an orally administered questionnaire to 60 children, split evenly between human and robotic interviewers, revealed that few significant differences in reporting were encountered between interviewer types. Overall 9 of 60 (15%) of participants reported being bullied in the past month. Participants were significantly more likely to report that fellow students were teased about their looks to the robot interviewer in comparison to the human interviewer. In addition to the examination of these results, a discussion of lessons learned for future studies of this nature are provided.

This research explored whether robots can use modern speech synthesizers to convey emotion with their speech. We investigated the use of MARY, an open source speech synthesizer, to convey a robot's emotional intent to novice robot users. The first experiment indicated that participants were able to distinguish the intended emotions of anger, calm, fear, and sadness with success rates of 65.9%, 68.9%, 33.3%, and 49.2%, respectively. An issue was the recognition rate of the intended happiness statements, 18.2%, which was below the 20% level determined for chance. The vocal prosody modifications for the expression of happiness were adjusted and the recognition rates for happiness improved to 30.3% in a second experiment. This is an important benchmarking step in a line of research that investigates the use of emotional speech by robots to improve human-robot interaction. Recommendations and lessons learned from this research are presented.

This paper introduces and empirically evaluates two scaling functions to alter a robot's physical movements based on proximity to a human. Previous research has focused on individual aspects of proxemics, like the appropriate distance to maintain from a human, but has not explored autonomous methods to adapt robot behavior as proximity changes. This paper proposes that robots in a social role should modify their behavior using a continuous function mapped to proximity. The method developed calculates a gain value from proximity readings, which is used to shape the execution of active behaviors on the robot. In order to identify the effects of different mappings from proximity to gain value, two different scaling functions were implemented on an affective search and rescue robot. The findings from a 72 participant study, in a high-fidelity mock disaster site, are examined with attention given to a new measure to determine proxemic awareness. The results indicated that for attributes of intelligence, likability, proxemic awareness, and submissiveness, a logarithmic-based scaling function is preferred over a linear-based scaling function, and over no scaling function. In areas of participant comfort and participant stress, the results indicated both logarithmic and linear scaling functions were preferred to no scaling.

This study demonstrates that robots can achieve socially acceptable interactions using loosely synchronized head gaze-speech acts. Prior approaches use tightly synchronized head gaze-speech, which requires significant human effort and time to manually annotate synchronization events in advance, restricts interactive dialog, or requires that the operator acts as a puppeteer. This paper describes how autonomous synchronization of head gaze can be achieved by exploiting affordances in the sentence structure and time delays. A 93-participant user study was conducted in a simulated disaster site. The rescue robot “Survivor Buddy” generated head gaze for a victim management scenario using a 911 dialog. The study used pre- and postinteraction questionnaires to compare the social acceptance level of loosely synchronized head gaze-speech against tightly synchronized head gaze-speech (manual annotation) and no head gaze-speech conditions. The results indicated that for attributes of Self-Assessment Manikin, i.e., Arousal, Robot Likeability, Human-Like Behavior, Understanding Robot Behavior, Gaze-Speech Synchronization, Looking at Objects at Appropriate Times, and Natural Movement, the loosely synchronized head gaze-speech is similar to tightly synchronized head gaze-speech and preferred to the no head gaze-speech case. This study contributes to a fundamental understanding of the role of social head gaze in social acceptance for human-machine interaction, how social gaze can be produced, and promotes practical implementation in social robots.

Humans regulate their social behavior based on proximity to other social actors. Likewise, when a robot fulfills the role of a social actor it too should regulate its interaction based on proximity. This paper describes work in progress to establish methods for autonomous modification of social behavior based on proximity and to quantify human preferences between methods of scaling a robot's social behaviors based on distance from a human. The preliminary results of a 72 participant human study examine the reaction to scaling with linear methods and perception-based methods. Results indicate significantly higher ratings in multiple areas (comfort, natural movement, safety, self-control, intelligence, likability, submissiveness (p<.05) when using a perception-based scaling function, as opposed to a linear or no scaling function. Work in progress is analyzing the biometric measures collected.

This paper presents preliminary research investigating whether preschool children (ages four to six years old) would be as comfortable sharing a secret they had been told not to share, with a humanoid robot as they would an adult, to explore the possible future use of robots to gather sensitive information from children that may have experienced maltreatment. The children in this research played the game “follow-the-leader” with an adult and a humanoid robot. As part of this research, the lead investigator shared a unique secret with each child. During a break in the “follow-the-leader” game with the adult and the robot, the children were prompted with five questions to determine if they would share the secret they were told by the investigator. The qualitative results from the study indicate that the children were as likely to share the secret with the robot as the adult with a similar amount of prompting effort. Additionally, the children interacted with the robot using similar social conventions (e.g., greeting, turn-taking, etc) as observed in their interactions with the adult.

This video presents a visual summary of large-scale, complex human study in Human-Robot Interaction (HRI) designed to evaluate whether humans would view interactions with two non-anthropomorphic robots more positively and calming when the robots were operated in an emotive mode versus a standard, non-emotive mode. The video presents actual participants' reactions, the study design, and images from search and rescue operations.

This paper describes preliminary results of a large-scale, complex human study in HRI in which results show that participants were calmer interacting with non-anthropomorphic robots operated in an emotive mode versus a standard, non-emotive mode.

Non-facial and non-verbal methods of affective expression are essential for naturalistic social interaction in robots that are designed to be functional and lack expressive faces (appearance-constrained) such as those used in search and rescue, law enforcement, and military applications. This correspondence identifies five main methods of non-facial and non-verbal affective expression (body movement, posture, orientation, color, and sound), and ranks their effectiveness forappearance-constrainedrobots operating within theintimate, personal, andsocialproximity zones of a human corresponding to interagent distances of approximately 3 m or less. This distance is significant because it encompasses the most common human social interaction distances, the exception being thepublicdistance zone used for formal presentations. The correspondence complements prior, broad surveys of affective expression by reviewing the psychology, computer science, and robotics literature specifically relating the impact of social interaction in non-anthropomorphic andappearance-constrainedrobots, and summarizing robotic implementations that utilize non-facial and non-verbal methods of affective expression as their primary means of expression. The literature is distilled into a set of prescriptive recommendations of the appropriate affective expression methods for each of the three proximity zones of interest. These recommendations serve as design guidelines for retroactively adding affective expression through software to a robot without physical modifications or designing a new robot.

This work applies a previously developed set of heuristics for determining when to use non-facial/non-verbal methods of affective expression to the domain of a robot being used for victim assessment in the aftermath of a disaster. Robot-assisted victim assessment places a robot approximately three meters or less from a victim, and the path of the robot traverses three proximity zones (intimate (contact -- 0.46m), personal (0.46 -- 1.22 m), and social (1.22 -- 3.66 m)). Robot- and victim-eye views of an Inuktun robot were collected as it followed a path around the victim. The path was derived from observations of a prior robot-assisted medical reachback study. The victim's-eye views of the robot from seven points of interest on the path illustrate the appropriateness of each of the five primary non-facial/non-verbal methods of affective expression: (body movement, posture, orientation, illuminated color, and sound), offering support for the heuristics as a design aid. In addition to supporting the heuristics, the investigation identified three open research questions on acceptable motions and impact of the surroundings on robot affect.

This paper reviews the literature related to the use of psychophysiology measures in human-robot interaction (HRI) studies in an effort to address the fundamental question of appropriate metrics and methodologies for evaluating HRI research, especially affect. It identifies four main methods of evaluation in HRI studies: (1) self-report measures, (2) behavioral measures, (3) psychophysiology measures, and (4) task performance. However, the paper also shows that using only one of these measures for evaluation is insufficient to provide a complete evaluation and interpretation of the interactions between a robot and the human with which it is interacting. In addition, the paper describes exemplar HRI studies which use psychophysiological measures; these implementations fall into three categories: detection and/or identification of specific emotions of participants from physiological signals, evaluation of participants' responses to a robot through physiological signals, and development and implementation of real-time control and modification of robot behaviors using physiological signals. Two open research questions on psychophysiological metrics were identified as a result of this review.

Software-reliability models (SRMs) are used for the assessment and improvement of reliability in software systems. These models are normally based on stochastic processes, with the nonhomogeneous Poisson process being one of the most prominent model forms. An underlying assumption of these models is that software failures occur randomly in time. This assumption has never been quantitatively tested. Our contribution in this paper is to conduct an experimental investigation that contrasts random processes with nonlinear deterministic processes as a model for software failures. We study two sets of real-world software-reliability data using the techniques of chaotic time-series analysis. We have found that both appear to arise from a deterministic process, rather than a stochastic process, and that both show some evidence of chaotic dynamics. In addition, we have conducted a series of k-steps-ahead forecasting experiments in the datasets, pitting a number of well-known stochastic SRMs against radial basis function networks (RBFNs), which are deterministic in nature. The out-of-sample prediction results from the RBFNs showed an improvement of roughly 25% over the best of the stochastic models, for both of our datasets. Finally, we propose a causal model to explain these results, which hypothesizes that faults in a program are distributed over a fractal subset of the program's input space

No abstract available.