It's not what you think: shaping beliefs about a robot to influence a teleoperator's expectations and behavior

In this paper we present a novel design approach for shaping a teleoperator's expectations and behaviors when teleoperating a robot. Just as how people may drive a car differently based on their expectations of it (e.g., the brakes may be poor), we assert that teleoperators may likewise operate a robot differently based on expectations of robot capability and robustness. We present 3 novel interaction designs that proactively shape teleoperator perceptions, and the results from formal studies that demonstrate that these techniques do indeed shape operator perceptions, and in some cases, measures of driving behavior such as changes in collisions. Our methods shape operator perceptions of a robot's speed, weight, or overall safety, designed to encourage them to drive more safely. This approach shows promise as an avenue for improving teleoperator effectiveness without requiring changes to a robot, novel sensors, algorithms, or other functionality.

Living Lab-Based Service Interaction Design for a Companion Robot for Seniors in South Korea

A living lab is a valuable method for designing tangible and intangible service elements, ensuring a comprehensive user experience. Developing a digital companion service, which users may be unfamiliar with, requires observing user behavior in real-world environments and analyzing living and behavioral patterns. A living lab starts with understanding user characteristics and behaviors. Living lab methods have an impact on the accuracy and precision of service design. The number of seniors in South Korea is rapidly increasing, leading to a rise in social issues like solitary deaths and suicide. Addressing these problems has led to a growing demand for companion robots. To design effective companion services, understanding seniors' living environments and their cognitive and behavioral traits is essential. This opinion piece, based on a national R&D project, presents the development of a digital companion for seniors. It offers insights, providing a comprehensive overview of living lab-based service interaction design and proposing methodologies about living lab environment construction and experimentation and considerations when designing robot interaction functions and appearance. The living lab environment includes real living spaces, laboratories, virtual reality settings, and senior welfare centers. Using the research findings, we created service scenarios, analyzed senior language characteristics, and developed the concept and facial expressions of the digital companion. To successfully introduce a novel service, it is crucial to analyze users' real-life behavior and adjust the service accordingly.

The future of mobility-as-a-service: trust transfer across automated mobilities, from road to sidewalk

While trust in different types of automated vehicles has been a major focus for researchers and vehicle manufacturers, few studies have explored how people trust automated vehicles that are not cars, nor how their trust may transfer across different mobilities enabled with automation. To address this objective, a dual mobility study was designed to measure how trust in an automated vehicle with a familiar form factor-a car-compares to, and influences, trust in a novel automated vehicle-termed sidewalk mobility. A mixed-method approach involving both surveys and a semi-structured interview was used to characterize trust in these automated mobilities. Results found that the type of mobility had little to no effect on the different dimensions of trust that were studied, suggesting that trust can grow and evolve across different mobilities when the user is unfamiliar with a novel automated driving-enabled (AD-enabled) mobility. These results have important implications for the design of novel mobilities.

Wearable Drone Controller: Machine Learning-Based Hand Gesture Recognition and Vibrotactile Feedback

We proposed a wearable drone controller with hand gesture recognition and vibrotactile feedback. The intended hand motions of the user are sensed by an inertial measurement unit (IMU) placed on the back of the hand, and the signals are analyzed and classified using machine learning models. The recognized hand gestures control the drone, and the obstacle information in the heading direction of the drone is fed back to the user by activating the vibration motor attached to the wrist. Simulation experiments for drone operation were performed, and the participants' subjective evaluations regarding the controller's convenience and effectiveness were investigated. Finally, experiments with a real drone were conducted and discussed to validate the proposed controller.

A Unifying Framework for Human-Agent Collaborative Systems-Part II: Design Procedure and Application

The human-agent collaboration (HAC) is a prospective research topic, whose great applications and future scenarios have attracted vast attention. It is very important to understand the design process of the HAC system (HACS). Inspired by the systematic analysis framework presented in Part I of this dual publication, this article proposes a normalized two-phase procedure, namely, GET-MAN, for the top-level design of HACS from the perspective of system engineering. The two-phase design procedure can produce a coherent and well-running HACS by sophisticatedly and properly determining the six elements of the HACS and their influences. In the verification phase of GET-MAN, by applying the formalized HACS framework proposed in Part I, a formal model can be constructed to look ahead (predict) and back (explain) at potential faults in the candidate HACS. An example of the HACS design for target searching is employed to illustrate the use of the GET-MAN design procedure. The potential challenges and future research directions are discussed in the light of the GET-MAN design procedure. The systematic analysis framework, Part I, as well as the GET-MAN design procedure, Part II, can serve as common guidance and reference for analyzing and developing various HACSs.

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones

In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.

Trust Measurement in Human-Autonomy Teams: Development of a Conceptual Toolkit

The rise in artificial intelligence capabilities in autonomy-enabled systems and robotics has pushed research to address the unique nature of human-autonomy team collaboration. The goal of these advanced technologies is to enable rapid decision making, enhance situation awareness, promote shared understanding, and improve team dynamics. Simultaneously, use of these technologies is expected to reduce risk to those who collaborate with these systems. Yet, for appropriate human- autonomy teaming to take place, especially as we move beyond dyadic partnerships, proper calibration of team trust is needed to effectively coordinate interactions during high-risk operations. But to meet this end, critical measures of team trust for this new dynamic of human-autonomy teams are needed. This paper seeks to expand on trust measurement principles and the foundation of human-autonomy teaming to propose a “toolkit” of novel methods that support the development, maintenance and calibration of trust in human-autonomy teams operating within uncertain, risky, and dynamic environments.

Intuitive, Efficient and Ergonomic Tele-Nursing Robot Interfaces: Design Evaluation and Evolution

Tele-nursing robots provide a safe approach for patient-caring in quarantine areas. For effective nurse-robot collaboration, ergonomic teleoperation and intuitive interfaces with low physical and cognitive workload must be developed. We propose a framework to evaluate the control interfaces to iteratively develop an intuitive, efficient, and ergonomic teleoperation interface. The framework is a hierarchical procedure that incorporates general to specific assessment and its role in design evolution. We first present pre-defined objective and subjective metrics used to evaluate three representative contemporary teleoperation interfaces. The results indicate that teleoperation via human motion mapping outperforms the gamepad and stylus interfaces. The trade-off with using motion mapping as a teleoperation interface is the non-trivial physical fatigue. To understand the impact of heavy physical demand during motion mapping teleoperation, we propose an objective assessment of physical workload in teleoperation using electromyography (EMG). We find that physical fatigue happens in the actions that involve precise manipulation and steady posture maintenance. We further implemented teleoperation assistance in the form of shared autonomy to eliminate the fatigue-causing component in robot teleoperation via motion mapping. The experimental results show that the autonomous feature effectively reduces the physical effort while improving the efficiency and accuracy of the teleoperation interface.

Still Not Solved: A Call for Renewed Focus on User-Centered Teleoperation Interfaces

Teleoperation is one of the oldest applications of human-robot interaction, yet decades later, robots are still difficult to control in a variety of situations, especially when used by non-expert robot operators. That difficulty has relegated teleoperation to mostly expert-level use cases, though everyday jobs and lives could benefit from teleoperated robots by enabling people to get tasks done remotely. Research has made great progress by improving the capabilities of robots, and exploring a variety of interfaces to improve operator performance, but many non-expert applications of teleoperation are limited by the operator’s ability to understand and control the robot effectively. We discuss the state of the art of user-centered research for teleoperation interfaces along with challenges teleoperation researchers face and discuss how an increased focus on human-centered teleoperation research can help push teleoperation into more everyday situations.

A Unifying Framework for Human-Agent Collaborative Systems-Part I: Element and Relation Analysis

The human-agent collaboration (HAC) is a prospective research topic whose great applications and future scenarios have attracted vast attention. In a broad sense, the HAC system (HACS) can be broken down into six elements: "Man," "Agents," "Goal," "Network," "Environment," and "Tasks." By merging these elements and building a relation graph, this article proposes a systematic analysis framework for HACS, and attempts to make a comprehensive analysis of these elements and their relationships. We coin the abbreviation "MAGNET" to name the framework by stringing together the initials of the above six terms. The framework provides novel insights into analyzing various HAC patterns and integrates different types of HACSs in a unifying way. The presentation of the HACS framework is divided into two parts. This article, part I, presents the systematic analysis framework. Part II proposes a normalized two-stage top-level design procedure for designing an HACS from the perspective of MAGNET.

Robot Transparency and Anthropomorphic Attribute Effects on Human-Robot Interactions

Anthropomorphic robots need to maintain effective and emotive communication with humans as automotive agents to establish and maintain effective human-robot performances and positive human experiences. Previous research has shown that the characteristics of robot communication positively affect human-robot interaction outcomes such as usability, trust, workload, and performance. In this study, we investigated the characteristics of transparency and anthropomorphism in robotic dual-channel communication, encompassing the voice channel (low or high, increasing the amount of information provided by textual information) and the visual channel (low or high, increasing the amount of information provided by expressive information). The results showed the benefits and limitations of increasing the transparency and anthropomorphism, demonstrating the significance of the careful implementation of transparency methods. The limitations and future directions are discussed.

Generating Legible and Glanceable Swarm Robot Motion through Trajectory, Collective Behavior, and Pre-attentive Processing Features

As swarm robots begin to share the same space with people, it is critical to design legible swarm robot motion that clearly and rapidly communicates the intent of the robots to nearby users. To address this, we apply concepts from intent-expressive robotics, swarm intelligence, and vision science. Specifically, we leverage the trajectory, collective behavior, and density of swarm robots to generate motion that implicitly guides people’s attention toward the goal of the robots. Through online evaluations, we compared different types of intent-expressive motions both in terms of legibility as well as glanceability, a measure we introduce to gauge an observer’s ability to predict robots’ intent pre-attentively. The results show that the collective behavior-based motion has the best legibility performance overall, whereas, for glanceability, trajectory-based legible motion is most effective. These results suggest that the optimal solution may involve a combination of these legibility cues based on the scenario and the desired properties of the motion.

Can Robots Earn Our Trust the Same Way Humans Do? A Systematic Exploration of Competence, Warmth, and Anthropomorphism as Determinants of Trust Development in HRI

Robots increasingly act as our social counterparts in domains such as healthcare and retail. For these human-robot interactions (HRI) to be effective, a question arises on whether we trust robots the same way we trust humans. We investigated whether the determinants competence and warmth, known to influence interpersonal trust development, influence trust development in HRI, and what role anthropomorphism plays in this interrelation. In two online studies with 2 × 2 between-subjects design, we investigated the role of robot competence (Study 1) and robot warmth (Study 2) in trust development in HRI. Each study explored the role of robot anthropomorphism in the respective interrelation. Videos showing an HRI were used for manipulations of robot competence (through varying gameplay competence) and robot anthropomorphism (through verbal and non-verbal design cues and the robot's presentation within the study introduction) in Study 1 (n = 155) as well as robot warmth (through varying compatibility of intentions with the human player) and robot anthropomorphism (same as Study 1) in Study 2 (n = 157). Results show a positive effect of robot competence (Study 1) and robot warmth (Study 2) on trust development in robots regarding anticipated trust and attributed trustworthiness. Subjective perceptions of competence (Study 1) and warmth (Study 2) mediated the interrelations in question. Considering applied manipulations, robot anthropomorphism neither moderated interrelations of robot competence and trust (Study 1) nor robot warmth and trust (Study 2). Considering subjective perceptions, perceived anthropomorphism moderated the effect of perceived competence (Study 1) and perceived warmth (Study 2) on trust on an attributional level. Overall results support the importance of robot competence and warmth for trust development in HRI and imply transferability regarding determinants of trust development in interpersonal interaction to HRI. Results indicate a possible role of perceived anthropomorphism in these interrelations and support a combined consideration of these variables in future studies. Insights deepen the understanding of key variables and their interaction in trust dynamics in HRI and suggest possibly relevant design factors to enable appropriate trust levels and a resulting desirable HRI. Methodological and conceptual limitations underline benefits of a rather robot-specific approach for future research.

Combining cognitive work analysis and empirical evaluations to understand map use by operators of small carry-on unmanned aerial systems

Operating a small carry-on unmanned aerial system (UAS) alone is challenging. Research on facilitating single-operator work has focused mainly on payload operation and health monitoring. Little focus has been given to mission-related aspects and how the command and control (C2) map display contributes to mission accomplishment. This study uses cognitive work analysis (CWA) to describe the operational work of the mission operator of a Skylark miniature UAS system. Three CWA phases were conducted - work domain analysis, control task analysis and strategy analysis - providing a rich framework of operational mission phases, task components, processes and the physical interface-objects in use. These representations highlight the operators' extensive use of the C2 map during all mission phases, for all object-related processes. To further enhance the outcomes of the CWA, and prior to outlining specific design requirements, an empirical investigation was conducted in which the eye movements of five experienced operators were obtained during a simulated mission. The empirical results confirm and further specify the work patterns that operators adopt. Quantitative analysis shows operators' extensive focus on the map, especially during mission-critical phases. These analyses led to the conclusion that a significant change in the way operators interact with the C2 map, or alternative designs to enhance map-based information utilization, should be applied. Insights drawn from this analysis can be applied to other aerial surveillance work domains, and adding empirical evaluations is helpful to further refine and reinforce the CWA outcomes.

Enhancement and Application of a UAV Control Interface Evaluation Technique

UAV supervisory control interfaces are important for safe operations and mission performance. We reviewed existing UAV interface design and evaluation tools and identified limitations. To address issues with existing methods, we developed an enhanced evaluation tool, the M-GEDIS-UAV. The tool includes detailed criteria for all aspects of UAV control interface design to support operator performance. It also supports quantitative and objective assessment of an interface. We prototyped three UAV information displays, including a digital control display, analog control display, and “massive” data display, as part of a simulated supervisory control interface. Six analysts, including three human factors experts and three novices evaluated the interfaces using the M-GEDIS-UAV. Inter-rater reliability was high for the human factors experts, suggesting training in usability analysis is necessary for tool application. Results also revealed the massive data display to produce significantly lower evaluation scores than the other displays. We concluded that the M-GEDIS-UAV was sensitive to interface manipulations and was most effectively used by human factors experts. Using the M-GEDIS-UAV tool can reveal the majority of design deviations from guidelines early in the design process toward increasing the effectiveness of control interfaces.

Human-automation interaction for multiple robot control: the effect of varying automation assistance and individual differences on operator performance

In a human-automation interaction study, automation assistance level (AL) was investigated for its effects on operator performance in a dynamic, multi-tasking environment. Participants supervised a convoy of manned and unmanned vehicles traversing a simulated environment in three AL conditions, while maintaining situation awareness and identifying targets. Operators' situation awareness, target detection performance, workload and individual differences were evaluated. Results show increasing AL generally improved task performance and decreased perceived workload, however, differential effects due to operator spatial ability and perceived attentional control were found. Eye-tracking measures were useful in parsing out individual differences that subjective measures did not detect. At the highest AL, participants demonstrated potentially complacent behaviour, indicating task disengagement. Practitioner Summary: The effect of varying automation assistance level (AL) on operator performance on multiple tasks were examined in a within-subjects experiment. Findings indicated a moderate AL improved performance, while higher levels encouraged complacent behaviour. Effects due to individual differences suggest that effective AL depends on the underlying characteristics of the operator.

Robotic assistance in the coordination of patient care

We conducted a study to investigate trust in and dependence upon robotic decision support among nurses and doctors on a labor and delivery floor. There is evidence that suggestions provided by embodied agents engender inappropriate degrees of trust and reliance among humans. This concern represents a critical barrier that must be addressed before fielding intelligent hospital service robots that take initiative to coordinate patient care. We conducted our experiment with nurses and physicians, and evaluated the subjects’ levels of trust in and dependence upon high- and low-quality recommendations issued by robotic versus computer-based decision support. The decision support, generated through action-driven learning from expert demonstration, produced high-quality recommendations that were accepted by nurses and physicians at a compliance rate of 90%. Rates of Type I and Type II errors were comparable between robotic and computer-based decision support. Furthermore, embodiment appeared to benefit performance, as indicated by a higher degree of appropriate dependence after the quality of recommendations changed over the course of the experiment. These results support the notion that a robotic assistant may be able to safely and effectively assist with patient care. Finally, we conducted a pilot demonstration in which a robot-assisted resource nurses on a labor and delivery floor at a tertiary care center.

Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction

Multi-robot missions are a challenge for operators in terms of workload and situational awareness. These operators have to receive data from the robots, extract information, understand the situation properly, make decisions, generate the adequate commands, and send them to the robots. The consequences of excessive workload and lack of awareness can vary from inefficiencies to accidents. This work focuses on the study of future operator interfaces of multi-robot systems, taking into account relevant issues such as multimodal interactions, immersive devices, predictive capabilities and adaptive displays. Specifically, four interfaces have been designed and developed: a conventional, a predictive conventional, a virtual reality and a predictive virtual reality interface. The four interfaces have been validated by the performance of twenty-four operators that supervised eight multi-robot missions of fire surveillance and extinguishing. The results of the workload and situational awareness tests show that virtual reality improves the situational awareness without increasing the workload of operators, whereas the effects of predictive components are not significant and depend on their implementation.

The Effects of Information Level on Human-Agent Interaction for Route Planning

Two experiments were conducted to examine the effects of level of information on human operators’ route selection decisions. Experiment 1 examined how information about resource usage/requirements affected route selection decisions for a remotely based supervisor guiding a dismounted soldier unit through an urban environment. Experiment 2 increased the level of information from Experiment 1 by adding a robotic asset to the unit and providing its resource usage/requirements. Decision time increased as the level of information increased and increased again with the addition of the robotic asset. In addition, as the level of information increased, preference for specific information sources began to vary. In the condition with the greatest level of information available, participants displayed no clear consensus as to preferred information source, with many indicating they relied upon sources that were unsuitable for successful mission completion. Future research could investigate further into the complexity of appropriate display for user interfaces involving robotic assets.

Mission Command in the Age of Network-Enabled Operations: Social Network Analysis of Information Sharing and Situation Awareness

A common assumption in organizations is that information sharing improves situation awareness and ultimately organizational effectiveness. The sheer volume and rapid pace of information and communications received and readily accessible through computer networks, however, can overwhelm individuals, resulting in data overload from a combination of diverse data sources, multiple data formats, and large data volumes. The current conceptual framework of network enabled operations (NEO) posits that robust networking and information sharing act as a positive feedback loop resulting in greater situation awareness and mission effectiveness in military operations (Alberts and Garstka, 2004). We test this assumption in a large-scale, 2-week military training exercise. We conducted a social network analysis of email communications among the multi-echelon Mission Command staff (one Division and two sub-ordinate Brigades) and assessed the situational awareness of every individual. Results from our exponential random graph models challenge the aforementioned assumption, as increased email output was associated with lower individual situation awareness. It emerged that higher situation awareness was associated with a lower probability of out-ties, so that broadly sending many messages decreased the likelihood of attaining situation awareness. This challenges the hypothesis that increased information sharing improves situation awareness, at least for those doing the bulk of the sharing. In addition, we observed two trends that reflect a compartmentalizing of networked information sharing as email links were more commonly formed among members of the command staff with both similar functions and levels of situation awareness, than between two individuals with dissimilar functions and levels of situation awareness; both those findings can be interpreted to reflect effects of homophily. Our results have major implications that challenge the current conceptual framework of NEO. In addition, the information sharing network was largely imbalanced and dominated by a few key individuals so that most individuals in the network have very few email connections, but a small number of individuals have very many connections. These results highlight several major growing pains for networked organizations and military organizations in particular.

Intelligent Agent Transparency in Human-Agent Teaming for Multi-UxV Management

OBJECTIVE

We investigated the effects of level of agent transparency on operator performance, trust, and workload in a context of human-agent teaming for multirobot management.

BACKGROUND

Participants played the role of a heterogeneous unmanned vehicle (UxV) operator and were instructed to complete various missions by giving orders to UxVs through a computer interface. An intelligent agent (IA) assisted the participant by recommending two plans-a top recommendation and a secondary recommendation-for every mission.

METHOD

A within-subjects design with three levels of agent transparency was employed in the present experiment. There were eight missions in each of three experimental blocks, grouped by level of transparency. During each experimental block, the IA was incorrect three out of eight times due to external information (e.g., commander's intent and intelligence). Operator performance, trust, workload, and usability data were collected.

RESULTS

Results indicate that operator performance, trust, and perceived usability increased as a function of transparency level. Subjective and objective workload data indicate that participants' workload did not increase as a function of transparency. Furthermore, response time did not increase as a function of transparency.

CONCLUSION

Unlike previous research, which showed that increased transparency resulted in increased performance and trust calibration at the cost of greater workload and longer response time, our results support the benefits of transparency for performance effectiveness without additional costs.

APPLICATION

The current results will facilitate the implementation of IAs in military settings and will provide useful data to the design of heterogeneous UxV teams.

Engineering Trust in Complex Automated Systems

We studied the transparency of automated tools used during emergency operations in commercial aviation. Transparency (operationalized as increasing levels of explanation associated with an automated tool recommendation) was manipulated to evaluate how transparent interfaces influence pilot trust of an emergency landing planning aid. We conducted a low-fidelity study in which commercial pilots interacted with simulated recommendations from NASA’s Emergency Landing Planner (ELP) that varied in their associated levels of transparency. Results indicated that trust in the ELP was influenced by the level of transparency within the human–machine interface of the ELP. Design recommendations for automated systems are discussed.

Individual Differences in Human-Agent Teaming

Studies investigating the effects of level of autonomy (LOA) on workload and performance in human-agent teams typically utilize subjective measures, but do not often incorporate physiological measures. This paper examines how well eye movement data collected in a recent experiment converges with the findings suggested by the subjective measures. Several eye behavior measures (fixation count, average fixation duration, blink rate, saccade amplitude and pupil diameter) were evaluated, and findings based on these compared to findings of NASA-TLX and situation awareness questionnaires. In addition, individual differences due to perceived attentional control were evaluated. Findings indicate that the physiological measures account for variance in workload that typical subjective measures may not, as they are more sensitive to individual differences among participants.

Interactive effects of the COMT gene and training on individual differences in supervisory control of unmanned vehicles

OBJECTIVE

We examined whether a gene known to influence dopamine availability in the prefrontal cortex is associated with individual differences in learning a supervisory control task.

BACKGROUND

Methods are needed for selection and training of human operators who can effectively supervise multiple unmanned vehicles (UVs). Compared to the valine (Val) allele, the methionine (Met) allele of the COMT gene has been linked to superior executive function, but it is not known whether it is associated with training-related effects in multi-UV supervisory control performance.

METHOD

Ninety-nine healthy adults were genotyped for the COMT Val158Met single nucleotide polymorphism (rs4680) and divided into Met/Met, Val/Met, and Val/Val groups. Participants supervised six UVs in an air defense mission requiring them to attack incoming enemy aircraft and protect a no-fly zone from intruders in conditions of low and high task load (numbers of enemy aircraft). Training effects were examined across four blocks of trials in each task load condition.

RESULTS

Compared to the Val/Met and Val/Val groups, Met/Met individuals exhibited a greater increase in enemy targets destroyed and greater reduction in enemy red zone incursions across training blocks.

CONCLUSION

Individuals with the COMT Met/Met genotype can acquire skill in executive function tasks, such as multi-UV supervisory control, to a higher level and/or faster than other genotype groups.

APPLICATION

Potential applications of this research include the development of individualized training methods for operators of multi-UV systems and selecting personnel for complex supervisory control tasks.

The construct of state-level suspicion: a model and research agenda for automated and information technology (IT) contexts

OBJECTIVE

The objective was to review and integrate available research about the construct of state-level suspicion as it appears in social science literatures and apply the resulting findings to information technology (IT) contexts.

BACKGROUND

Although the human factors literature is replete with articles about trust (and distrust) in automation, there is little on the related, but distinct, construct of "suspicion" (in either automated or IT contexts). The construct of suspicion--its precise definition, theoretical correlates, and role in such applications--deserves further study.

METHOD

Literatures that consider suspicion are reviewed and integrated. Literatures include communication, psychology, human factors, management, marketing, information technology, and brain/neurology. We first develop a generic model of state-level suspicion. Research propositions are then derived within IT contexts.

RESULTS

Fundamental components of suspicion include (a) uncertainty, (b) increased cognitive processing (e.g., generation of alternative explanations for perceived discrepancies), and (c) perceptions of (mal)intent. State suspicion is defined as the simultaneous occurrence of these three components. Our analysis also suggests that trust inhibits suspicion, whereas distrust can be a catalyst of state-level suspicion. Based on a three-stage model of state-level suspicion, associated research propositions and questions are developed. These propositions and questions are intended to help guide future work on the measurement of suspicion (self-report and neurological), as well as the role of the construct of suspicion in models of decision making and detection of deception.

CONCLUSION

The study of suspicion, including its correlates, antecedents, and consequences, is important. We hope that the social sciences will benefit from our integrated definition and model of state suspicion. The research propositions regarding suspicion in IT contexts should motivate substantial research in human factors and related fields.

Team performance in networked supervisory control of unmanned air vehicles: effects of automation, working memory, and communication content

OBJECTIVE

Assess team performance within a net-worked supervisory control setting while manipulating automated decision aids and monitoring team communication and working memory ability.

BACKGROUND

Networked systems such as multi-unmanned air vehicle (UAV) supervision have complex properties that make prediction of human-system performance difficult. Automated decision aid can provide valuable information to operators, individual abilities can limit or facilitate team performance, and team communication patterns can alter how effectively individuals work together. We hypothesized that reliable automation, higher working memory capacity, and increased communication rates of task-relevant information would offset performance decrements attributed to high task load.

METHOD

Two-person teams performed a simulated air defense task with two levels of task load and three levels of automated aid reliability. Teams communicated and received decision aid messages via chat window text messages.

RESULTS

Task Load x Automation effects were significant across all performance measures. Reliable automation limited the decline in team performance with increasing task load. Average team spatial working memory was a stronger predictor than other measures of team working memory. Frequency of team rapport and enemy location communications positively related to team performance, and word count was negatively related to team performance.

CONCLUSION

Reliable decision aiding mitigated team performance decline during increased task load during multi-UAV supervisory control. Team spatial working memory, communication of spatial information, and team rapport predicted team success.

APPLICATION

An automated decision aid can improve team performance under high task load. Assessment of spatial working memory and the communication of task-relevant information can help in operator and team selection in supervisory control systems.

Design and test of a situation-augmented display for an unmanned aerial vehicle monitoring task

In this study, a situation-augmented display for unmanned aerial vehicle (UAV) monitoring was designed, and its effects on operator performance and mental workload were examined. The display design was augmented with the knowledge that there is an invariant flight trajectory (formed by the relationship between altitude and velocity) for every flight, from takeoff to landing. 56 participants were randomly assigned to the situation-augmented display or a conventional display condition to work on 4 (number of abnormalities) x 2 (noise level) UAV monitoring tasks three times. Results showed that the effects of situation-augmented display on flight completion time and time to detect abnormalities were robust under various workload conditions, but error rate and perceived mental workload were unaffected by the display type. Results suggest that the UAV monitoring task is extremely difficult, and that display devices providing high-level situation-awareness may improve operator monitoring performance.

Human Interaction With Multiple Remote Robots

In this chapter, I review research involving remote human supervision of multiple unmanned vehicles (UVs) using command complexity as an organizing construct. Multi-UV tasks range from foraging, requiring little coordination among UVs, to formation following, in which UVs must function as a cohesive unit. Command complexity, the degree to which operator effort increases with the number of supervised UVs, is used to categorize human interaction with multiple UVs. For systems in which each UV requires the same form of attention (O( n)), effort increases linearly with the number of UVs. For systems in which the control of one UV is dependent upon another (O(> n)), additional UVs impose greater than linear increases due to the expense of coordination. For other systems, an operator interacts with an autonomously coordinating group, and effort is unaffected by group size (O(1)). Studies of human/multi-UV interaction can be roughly grouped into O( n) supervision, involving one-to-one control of individual UVs, or O(1) commanding, in which higher-level commands are directed to a group. Research in O( n) command has centered on round-robin control, neglect tolerance, and attention switching. Approaches to O(1) command are divided into systems using autonomous path planning only, plan libraries, human-steered planners, and swarms. Each type of system has its advantages. Less complete work in scalable displays for multiple UVs is reviewed. Mixing levels of command is probably necessary to supervise multiple UVs performing realistic tasks. Research in O( n) control is mature and can provide quantitative and qualitative guidance for design. Interaction with planners and swarms is less mature but more critical to developing effective multi-UV systems capable of performing complex tasks.

Head Tactile Communication

Although tactile applications have been explored heavily in the past decade, use on the head is rare. Army researchers are exploring the possibility of using a head-mounted tactile display to augment visual displays currently used for navigation. Such a tactile display has the potential to decrease the amount of information the user would otherwise process visually by off-loading the navigation task from the visual to the tactile modality while providing soldiers with a covert method of receiving directional information regarding a navigation or sniper detection task.

The Effects of Autonomy and Cognitive Abilities on Workload and Supervisory Control of Unmanned Systems

In this paper, we examine the influence of autonomy and cognitive ability on workload in unmanned systems. First, we outline prior research regarding the role of autonomy and operator selection in decreasing workload in the realm of human-robot interaction. Next, we discuss two aspects of cognitive ability (i.e., visualization and perceptual speed) explain differences between these constructs, and their influence on workload. Then, we describe the current study and discuss the effects of varying levels of autonomy, visualization, and perceptual speed on workload in a simulated reconnaissance mission. Finally, we explore the implications of our findings in terms of the influence of autonomy and operator selection and provide suggestions for future research.

Supervisory control of multiple robots in dynamic tasking environments

A military targeting environment was simulated to examine the effects of an intelligent route-planning agent RoboLeader, which could support dynamic robot re-tasking based on battlefield developments, on the performance of robotics operators. We manipulated the level of assistance (LOAs) provided by RoboLeader as well as the presence of a visualisation tool that provided feedback to the participants on their primary task (target encapsulation) performance. Results showed that the participants' primary task benefited from RoboLeader on all LOAs conditions compared to manual performance; however, visualisation had little effect. Frequent video gamers demonstrated significantly better situation awareness of the mission environment than did infrequent gamers. Those participants with higher spatial ability performed better on a secondary target detection task than did those with lower spatial ability. Finally, participants' workload assessments were significantly lower when they were assisted by RoboLeader than when they performed the target entrapment task manually. Practitioner Summary: This study demonstrated the utility of an intelligent agent for enhancing robotics operators' supervisory control performance as well as reducing their workload during a complex urban scenario involving moving targets. The results furthered the understanding of the interplay among level-of-autonomy, multitasking performance and individual differences in military tasking environments.

Supervisory control of multiple robots: effects of imperfect automation and individual differences

OBJECTIVE

A military multitasking environment was simulated to examine the effects of an intelligent agent, RoboLeader, on the performance of robotics operators.

BACKGROUND

The participants' task was to manage a team of ground robots with the assistance of RoboLeader, an intelligent agent capable of coordinating the robots and changing their routes on the basis of battlefield developments.

METHOD

In the first experiment, RoboLeader was perfectly reliable; in the second experiment, RoboLeader's recommendations were manipulated to be either false-alarm prone or miss prone, with a reliability level of either 60% or 90%. The visual density of the targeting environment was manipulated by the presence or absence of friendly soldiers.

RESULTS

RoboLeader, when perfectly reliable, was helpful in reducing the overall mission times.The type of RoboLeader imperfection (false-alarm vs. miss prone) affected operators' performance of tasks involving visual scanning (target detection, route editing, and situation awareness). There was a consistent effect of visual density (clutter of the visual scene) for multiple performance measures. Participants' attentional control and video gaming experience affected their overall multitasking performance. In both experiments, participants with greater spatial ability consistently outperformed their low-spatial-ability counterparts in tasks that required effective visual scanning.

CONCLUSION

Intelligent agents, such as RoboLeader, can benefit the overall human-robot teaming performance. However, the effects of type of agent unreliability, tasking requirements, and individual differences have complex effects on human-agent interaction.

APPLICATION

The current results will facilitate the implementation of robots in military settings and will provide useful data to designs of systems for multirobot control.

A meta-analysis of factors affecting trust in human-robot interaction

OBJECTIVE

We evaluate and quantify the effects of human, robot, and environmental factors on perceived trust in human-robot interaction (HRI).

BACKGROUND

To date, reviews of trust in HRI have been qualitative or descriptive. Our quantitative review provides a fundamental empirical foundation to advance both theory and practice.

METHOD

Meta-analytic methods were applied to the available literature on trust and HRI. A total of 29 empirical studies were collected, of which 10 met the selection criteria for correlational analysis and 11 for experimental analysis. These studies provided 69 correlational and 47 experimental effect sizes.

RESULTS

The overall correlational effect size for trust was r = +0.26,with an experimental effect size of d = +0.71. The effects of human, robot, and environmental characteristics were examined with an especial evaluation of the robot dimensions of performance and attribute-based factors. The robot performance and attributes were the largest contributors to the development of trust in HRI. Environmental factors played only a moderate role.

CONCLUSION

Factors related to the robot itself, specifically, its performance, had the greatest current association with trust, and environmental factors were moderately associated. There was little evidence for effects of human-related factors.

APPLICATION

The findings provide quantitative estimates of human, robot, and environmental factors influencing HRI trust. Specifically, the current summary provides effect size estimates that are useful in establishing design and training guidelines with reference to robot-related factors of HRI trust. Furthermore, results indicate that improper trust calibration may be mitigated by the manipulation of robot design. However, many future research needs are identified.

Can You Trust Your Robot?

It is proposed that trust is a critical element in the interactive relations between humans and the automated and robotic technology they create. This article presents (a) why trust is an important issue for this type of interaction, (b) a brief history of the development of human-robot trust issues, and (c) guidelines for input by human factors/ergonomics professionals to the design of human-robot systems with emphasis on trust issues. Our work considers trust an ongoing and dynamic dimension as robots evolve from simple tools to active, sentient teammates.