This thesis worked towards answering the following question: Where, if at all, do the beliefs and behaviors associated with interacting with a nonhuman agent deviate from how we treat a human? This was done by exploring the inter-related fields of Human-Computer and Human-Robot Interaction in the literature review, viewing them through the theoretical lens of anthropomorphism. A study was performed which looked at how 104 participants would attribute blame in a robotic surgery scenario, as detailed in a vignette. A majority of results were statistically non-significant, however, some results emerged which may imply a diffusion of responsibility in human-robot collaboration scenarios.

Unmanned ground vehicles (UGVs) can be used in the military to mitigate risks taken by soldiers as well as to provide solutions to physically demanding, dull or dangerous tasks. While there are benefits of using UGVs, there are also needs and limitations that come with them. This thesis explores what the end user, a light infantry battalion in the Swedish Armed Forces, wants in terms of functions for a UGV designed for military oper- ations in urban terrain. This was done through a explorative case study with focus groups, where soldiers and officers from the 31st Ranger Battalion got to use two different UGV prototypes to complete tasks. This was followed by semi structured group discussions, where needs, limitations and requirements were explored. The collected data was then analysed by a thematic analysis approach. The results from the thematic analysis found several reoccurring opinions regarding requirements from the focus groups. The requirements were grouped into four categories; (1) Speed, (2) Use cases, (3) Image generating sensors, and (4) Autonomous functions. In conclusion, this thesis identified a total of 13 requirements within the four categories. To summarise, these requirements imply that a UGV meant for military operations in urban terrain must be able to keep up with sprinting soldiers, provide visual cover, be able to interact with nearby objects, have several high quality sensors and robust autonomous functions, allowing the soldiers to focus on other things than controlling the UGV.

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The United States Army has moved into the 21st century with the intent of redesigning not only the force structure but also the methods by which we will fight and win our nation's wars. Fundamental in this restructuring is the development of the Future Combat Systems (FCS). In an effort to minimize exposure of front line soldiers the future Army will utilize unmanned assets for both information gathering and when necessary engagements. Yet this must be done judiciously, as the bandwidth for net-centric warfare is limited. The implication is that the FCS must be designed to leverage bandwidth in a manner that does not overtax computational resources. In this study alternatives for improving human performance during operation of teleoperated and semi-autonomous robots were examined. It was predicted that when operating both types of robots, frame delay of the semi-autonomous robot would improve performance because it would allow operators to concentrate on the constant workload imposed by the teleoperated while only allocating resources to the semi-autonomous during critical tasks. An additional prediction was that operators with high spatial ability would perform better than those with low spatial ability, especially when operating an aerial vehicle. The results can not confirm that frame delay has a positive effect on operator performance, though power may have been an issue, but clearly show that spatial ability is a strong predictor of performance on robotic asset control, particularly with aerial vehicles. In operating the UAV, the high spatial group was, on average, 30% faster, lazed 12% more targets, and made 43% more location reports than the low spatial group. The implications of this study indicate that system design should judiciously manage workload and capitalize on individual ability to improve performance and are relevant to system designers, especially in the military community.

The purpose of this research was to examine operator workload and performance in a high risk, multi-task environment. Specifically, the research examined if a gunner of a Future Combat System, such as a Mounted Combat System, could effectively detect targets in the immediate environment while concurrently operating robotic assets in a remote environment. It also analyzed possible effects of individual difference factors, such as spatial ability and attentional control, on operator performance and workload. The experimental conditions included a gunner baseline and concurrent task conditions where participants simultaneously performed gunnery tasks and one of the following tasks: monitor an unmanned ground vehicle (UGV) via a video feed (Monitor), manage a semi-autonomous UGV, and teleoperate a UGV (Teleop). The analysis showed that the asset condition significantly impacted gunnery performance with the gunner baseline having the highest number of targets detected (M = 13.600 , SD = 2.353), and concurrent Teleop condition the lowest (M = 9.325 , SD = 2.424). The research also found that high spatial ability participants tended to detect more targets than low spatial ability participants. Robotic task performance was also affect by the asset condition. The results showed that the robotic target detection rate was lower for the concurrent task conditions. A significant difference was seen between the UGV-baseline (80.1%) when participants performed UGV tasks only and UGV-concurrent conditions (67.5%) when the participants performed UGV tasks concurrently with gunnery tasks. Overall, this study revealed that there were performance decrements for the gunnery tasks as well as the robotic tasks when the tasks were performed concurrently.

Human-Robot Interaction (HRI) research is examining ways to make human-robot (HR) communication more natural. Incorporating natural communication techniques is expected to make HR communication seamless and more natural for humans. Humans naturally incorporate implicit levels of communication, and including implicit communication in HR communication should provide tremendous benefit. The aim for this work was to evaluate a model for humanrobot implicit communication. Specifically, the primary goal for this research was to determine whether humans can assign meanings to implicit cues received from autonomous robots as they do for identical implicit cues received from humans. An experiment was designed to allow participants to assign meanings to identical, implicit cues (pursuing, retreating, investigating, hiding, patrolling) received from humans and robots. Participants were tasked to view random video clips of both entity types, label the implicit cue, and assign a level of confidence in their chosen answer. Physiological data was tracked during the experiment using an electroencephalogram and eye-tracker. Participants answered workload and stress measure questionnaires following each scenario. Results revealed that participants were significantly more accurate with human cues (84%) than with robot cues (82%), however participants were highly accurate, above 80%, for both entity types. Despite the high accuracy for both types, participants remained significantly more confident in answers for humans (6.1) than for robots (5.9) on a confidence scale of 1 - 7. Subjective measures showed no significant differences for stress or mental workload across entities. Physiological measures were not significant for the engagement index across v entity, but robots resulted in significantly higher levels of cognitive workload for participants via the index of cognitive activity. The results of this study revealed that participants are more confident interpreting human implicit cues than identical cues received from a robot. However, the accuracy of interpreting both entities remained high. Participants showed no significant difference in interpreting different cues across entity as well. Therefore, much of the ability of interpreting an implicit cue resides in the actual cue rather than the entity. Proper training should boost confidence as humans begin to work alongside autonomous robots as teammates, and it is possible to train humans to recognize cues based on the movement, regardless of the entity demonstrating the movement.

In highly autonomous robotic systems, human operators are able to attend to their own, separate tasks, but robots still need occasional human intervention. In this scenario, it may be difficult for human operators to determine the status of the system and environment when called upon to aid the robot. The resulting lack of situation awareness (SA) is a problem common to other automated systems, and it can lead to poor performance and compromised safety. Existing research on this problem suggested that reliable automation of information processing, called diagnostic aiding, leads to better operator SA. The effects of unreliable diagnostic aiding, however, were not well understood. These effects are likely to depend on the ability of the operator to perform the task unaided. That is, under conditions in which the operator can reconcile their own sensing with that of the robot, the influence of unreliable diagnostic aiding may be more pronounced. When the robot is the only source of information for a task, these effects may be weaker or may reverse direction. The purpose of the current experiment was to determine if SA is differentially affected by unreliability at different levels of unaided human performance and at different stages of diagnostic aiding. This was accomplished by experimentally manipulating the stage of diagnostic aiding, robot reliability, and the ability of the operator to build SA unaided. Results indicated that while reliable diagnostic aiding is generally useful, unreliable diagnostic aiding has effects that depend on the amount of information available to operators in the environment. This research improves understanding of how robots can support operator SA and can guide the development of future robots so that humans are most likely to use them effectively.

The transition in robotics from tools to teammates has begun. However, the benefit autonomous robots provide will be diminished if human teammates misinterpret robot behaviors. Applying mental model theory as the organizing framework for human understanding of robots, the current empirical study examined the influence of task-role mental models of robots on the interpretation of robot motion behaviors, and the resulting impact on subjective ratings of robots. Observers (N = 120) were exposed to robot behaviors that were either congruent or incongruent with their task-role mental model, by experimental manipulation of preparatory robot task-role information to influence mental models (i.e., security guard, groundskeeper, or no information), the robot's actual task-role behaviors (i.e., security guard or groundskeeper), and the order in which these robot behaviors were presented. The results of the research supported the hypothesis that observers with congruent mental models were significantly more accurate in interpreting the motion behaviors of the robot than observers without a specific mental model. Additionally, an incongruent mental model, under certain circumstances, significantly hindered an observer's interpretation accuracy, resulting in subjective sureness of inaccurate interpretations. The strength of the effects that mental models had on the interpretation and assessment of robot behaviors was thought to have been moderated by the ease with which a particular mental model could reasonably explain the robot's behavior, termed mental model applicability. Finally, positive associations were found between differences in observers' interpretation accuracy and differences in subjective ratings of robot intelligence, safety, and trustworthiness. The current research offers implications for the relationships between mental model components, as well as implications for designing robot behaviors to appear more transparent, or opaque, to humans.

The goal of this project is to involve adolescents in the design of their own social robots, and to explore their concerns and opinions about social robots during the design process. To support their design efforts, I have developed a digital toolkit that includes features for customizing the appearance, personality, and reactive behaviors of the robots. In addition, this paper presents some of the adolescents’ views on gender and robots that were elicited during the project. The insights and feedback from the participants can inform the design of future outreach robots and improve their social interactions with adolescents. / Målet med detta projekt är att involvera ungdomar i designen av sina egna sociala robotar, och att utforska deras oro och åsikter om sociala robotar under designprocessen. För att stödja deras designinsatser har jag utvecklat en digital verktygslåda som innehåller funktioner för att anpassa robotarnas utseende, personlighet och reaktiva beteenden. Dessutom presenterar denna artikel några av ungdomarnas syn på genus och robotar som framkallades under projektet. Insikterna och feedbacken från deltagarna kan informera om utformningen av framtida uppsökande robotar och förbättra deras sociala interaktion med ungdomar.

Soft robots are inherently safer than traditional robots due to their compliance and high power density ratio resulting in lower accidental impact forces. Thus they are a natural option for human-robot interaction. This thesis specifically looked at human-robot co-manipulation which is defined as a human and a robot working together to move an object too large or awkward to be safely maneuvered by a single agent. To better understand how humans communicate while co-manipulating an object, this work looked at haptic interaction of human-human dyadic co-manipulation trials and studied some of the trends found in that interaction. These trends point to ways robots can effectively work with human partners in the future. Before successful human-robot co-manipulation with large-scale soft robots can be achieved, low-level joint angle control is needed. Low-level model predictive control of soft robot joints requires a sufficiently accurate model of the system. This thesis introduces a recursive Newton-Euler method for deriving the dynamics that is sufficiently accurate and accounts for flexible joints in an intuitive way. This model has been shown to be accurate to a median absolute error of 3.15 degrees for a three-link three-joint six degree of freedom soft robot arm. Once a sufficiently accurate model was developed, a gain-based evolutionary model predictive control (MPC) technique was formulated based on a previous evolutionary MPC technique. This new method is referred to as model evolutionary gain-based predictive control or MEGa-PC. This control law is compared to nonlinear evolutionary model predictive control (NEMPC). The new technique allows intentionally decreasing the control frequency to 10 Hz while maintaining control of the system. This is proven to help MPC solve more difficult problems by having the ability to extend the control horizon. This new controller is also demonstrated to work well on a three-joint three-link soft robot arm. Although complete physical human-robot co-manipulation is outside the scope of this thesis, this thesis covers three main building blocks for physical human and soft robot co-manipulation: human-human haptic communication, soft robot modeling, and model evolutionary gain-based predictive control.