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.

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.

CNC machines offer numerous advantages over conventional machining. It can be implemented in several ways and one such implementation is a drawing machine. In this bachelor thesis a voice controlled CNC plotter was designed, constructed and programmed. In order to create a better understanding of CNC and voice recognition, research questions were established and studied. The questions were mainly related to drawing speed as well as quality and accuracy of the voice recognition. The hardware of the plotter was mostly built out of 3D-printed parts as well as stepper motors, threaded rods and couplers for the movement system. The software of the plotter consisted of Arduino code, where instructions were written to make, for instance, the appropriate motor move. Tests were executed to gather data that later on were analysed. The analysis showed that the stepper motors and couplers had the greatest impact on the drawing speed as well as showing that the quality decreased when the speed increased. Furthermore the analysis showed that the voice recognition module achieved a high level of accuracy, however only when males spoke as it could not detect female voices. / CNC maskiner har flera fördelar jämfört med konventionella maskiner. De kan implementeras på en mängd olika sätt, exempelvis i en rit robot. I detta kandidatexamensarbete konstruerades och programmerades en röststyr dritrobot. För att erhålla en bättre uppfattning om CNC och röststyrning har två forskningsfrågor undersökts. Frågorna behandlar rithastighet, kvalite samt noggrannhet av röstigenkänningsmodulen. Hårdvaran består för det mesta av 3D-printade komponenter och gängade stänger som utgör rörelse systemet av roboten. Mjukvaran består endast av Arduino kod som innehåller instruktioner för exempelvis vilken motor som ska rotera. Flera experiment utfördes för att erhålla data som därefter analyserades. Analysen visade att stegmotorerna och axelkopplingarna hade störst påverkan på rithastigheten som i sin tur var en stor påverkande faktor för ritkvaliten. Ytterliggare analys visade att röstkortet hade hög noggrannhet men bara när en man talade då det inte kunde tolka kvinnliga röster.

Robot swarms have recently become of interest in both industry and academia for their potential to perform various difficult or dangerous tasks efficiently. As real robot swarms become more of a possibility, many desire swarms to be controlled or directed by a human, which raises questions regarding how that should be done. Part of the challenge of human-swarm interaction is the difficulty of understanding swarm state and how to drive the swarm to produce emergent behaviors. Human input could inhibit desirable swarm behaviors if their input is poor and has sufficient influence over swarm agents, affecting its overall performance. Thus, with too little influence, human input is useless, but with too much, it can be destructive. We suggest that there is some middle level, or interval, of human influence that allows the swarm to take advantage of useful human input while minimizing the effect of destructive input. Further, we propose that human-swarm interaction schemes can be designed to maintain an appropriate level of human influence over the swarm and maintain or improve swarm performance in the presence of both useful and destructive human input. We test this theory by implementing a piece of software to dynamically moderate influence and then testing it with a simulated honey bee colony performing nest site selection, simulated human input, and actual human input via a user study. The results suggest that moderating influence, as suggested, is important for maintaining high performance in the presence of both useful and destructive human input. However, while our software seems to successfully moderate influence with simulated human input, it fails to do so with actual human input.

This work adopts a feminist perspective to analyse and compare the patriarchal oppression and objectification of humans and robots in the Foundation universe. It analyses the relationships between males and females, between humans from different social and cultural backgrounds, and between humans and robots. The study attempts to capture changes that span a long period and multiple locations, exploring how those changes are triggered by different forms of patriarchal oppression and objectification. This essay concludes that various forms of patriarchal oppression and objectification exist in the beginning but start slowly disintegrating as humanity, guided by robots, move towards a greater goal of establishing a better society.

Multi-agent systems (MAS) have been of interest to many researchers during the last decades. This thesis focuses on multi-robot systems (MRS) and programmable matter as two types of MAS. Regarding MRS, the focus is on the 'mergeable nervous system' (MNS) concept which allows the robots to connect to one another and establish a communication network through self-organization and then use the network to temporarily report sensing events and cede authority to a single robot in the system. Here, in a collective perception scenario, we experimentally evaluate the performance of an MNS-enabled approach and compare it with that of several decentralized benchmark approaches. We show that an MNS-enabled approach is high-performing, fault-tolerant, and scalable, so it is an appropriate approach for MRS. As a goal of the thesis, using an MNS-enabled approach, we present for the first time a comprehensive comparison of control architectures in multi-robot systems, which includes a comparison of accuracy, efficiency, speed, energy consumption, scalability, and fault tolerance. Our comparisons provide designers of multi-robot systems with a better understanding for selecting the best-performing control depending on the system's objectives. Additionally, as a separate goal, we design a high-level leader based programmable matter, which can perform some basic primitive operations in a grid environment, and construct it using lower-level organisms. We design and implement deterministic algorithms for "curl" operation of this high-level matter, an instance of shape formation problem. We prove the correctness of the presented algorithms, analytically determine their complexity, and experimentally evaluate their performance.

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