PhD / This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability.

In designing and evaluating human-robot interactions and interfaces, researchers often use simulated robots because of the high cost of physical robots and time required to program them. However, it is important to consider how interaction with a simulated robot differs from a real robot; that is, do simulated robots provide authentic interaction? We contribute to a growing body of work that explores this question and maps out simulated-versus-real differences, by explicitly investigating empathy: how people empathize with a physical or simulated robot when something bad happens to it. Empathy is particularly relevant to social human-robot interaction (HRI) and is integral to, e.g., companion and care robots. To explore our question, we develop a convincing HRI scenario that induces people’s empathy toward a robot, and explore psychology work for an empathy-measuring instrument. To formally evaluate our scenario and the empathy-measuring instrument in HRI scenario, we conduct a comparative user study: in one condition, participants have the scenario which induces empathy, and for the other condition, we remove any empathy inducing activities of the robot. With the validated scenario and empathy measuring instrument, we conduct another user study to explore the difference between a real and a simulated robot in terms of people’s empathic response. Our results suggest that people empathize more with a physical robot than a simulated one, a finding that has important implications on the generalizability and applicability of simulated HRI work. As part of our exploration, we additionally present an original and reproducible HRI experimental design to induce empathy toward robots, and experimentally validated an empathy-measuring instrument from psychology for use with HRI.

Based on my analysis, I found that there are a variety of ways in which individuals interact with and emotionally engage with their Roomba iRobots, via participation in a brand community or through forms of anthropomorphism such as treating it as a pet or human. I explain that there is a spectrum regarding the extent to which individuals anthropomorphize their Roomba and emotionally engage with the device. The thesis concludes with the finding that some individuals emotionally engage with their Roomba in a significant way, while others desire a disconnection from their device. I end with the suggestion that sociologists continue to consider the implications of people’s increasing interactions with technological objects and further investigate different areas of human-robot emotional connection.

PhD / This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability.

High-risk, time-critical situations require trust for humans to interact with other agents even if they have never interacted with the agents before. In the near future, robots will perform tasks to help people in such situations, thus robots must understand why a person makes a trust decision in order to effectively aid the person. High casualty rates in several emergency evacuations motivate our use of this scenario as an example of a high-risk, time-critical situation. Emergency guidance robots can be stored inside of buildings then activated to search for victims and guide evacuees to safety. In this dissertation, we determined the conditions under which evacuees would be likely to trust a robot in an emergency evacuation. We began by examining reports of real-world evacuations and considering how guidance robots can best help. We performed two simulations of evacuations and learned that robots could be helpful as long as at least 30% of evacuees trusted their guidance instructions. We then developed several methods for a robot to communicate directional information to evacuees. After performing three rounds of evaluation using virtually, remotely and physically present robots, we concluded that robots should communicate directional information by gesturing with two arms. Next, we studied the effect of situational risk and the robot's previous performance on a participant's decision to use the robot during an interaction. We found that higher risk scenarios caused participants to align their self-reported trust with their decisions in a trust situation. We also discovered that trust in a robot drops after a single error when interaction occurs in a virtual environment. After an exploratory study in trust repair, we have learned that a robot can repair broken trust during the emergency by apologizing for its prior mistake or giving additional information relevant to the situation. Apologizing immediately after the error had no effect. Robots have the potential to save lives in emergency scenarios, but could have an equally disastrous effect if participants overtrust them. To explore this concept, we created a virtual environment of an office as well as a real-world simulation of an emergency evacuation. In both, participants interacted with a robot during a non-emergency phase to experience its behavior and then chose whether to follow the robot’s instructions during an emergency phase or not. In the virtual environment, the emergency was communicated through text, but in the real-world simulation, artificial smoke and fire alarms were used to increase the urgency of the situation. In our virtual environment, we confirmed our previous results that prior robot behavior affected whether participants would trust the robot or not. To our surprise, all participants followed the robot in the real-world simulation of an emergency, despite half observing the same robot perform poorly in a navigation guidance task just minutes before. We performed additional exploratory studies investigating different failure modes. Even when the robot pointed to a dark room with no discernible exit the majority of people did not choose to exit the way they entered. The conclusions of this dissertation are based on the results of fifteen experiments with a total of 2,168 participants (2,071 participants in virtual or remote studies conducted over the internet and 97 participants in physical studies on campus). We have found that most human evacuees will trust an emergency guidance robot that uses understandable information conveyance modalities and exhibits efficient guidance behavior in an evacuation scenario. In interactions with a virtual robot, this trust can be lost because of a single error made by the robot, but a similar effect was not found with real-world robots. This dissertation presents data indicating that victims in emergency situations may overtrust a robot, even when they have recently witnessed the robot malfunction. This work thus demonstrates concerns which are important to both the HRI and rescue robot communities.

Developing robots that are useful to older adults is more than simply creating robots that complete household tasks. To ensure that older adults perceive a robot to be useful, careful consideration of the users’ capabilities, robot autonomy, and task is needed (Venkatesh & Davis, 2000). The purpose of this study was to investigate the construct of perceived usefulness within the context of robot assistance. Mobile older adults (N = 12) and older adults with mobility loss (N=12) participated in an autonomy selection think aloud task, and a persona based interview. Findings suggest that older adults with mobility loss preferred an autonomy level where they command/control the robot themselves. Mobile older adults’ preferences were split between commanding/controlling the robot themselves, or the robot commands/controls itself. Reasons for their preferences were related to decision making, and were task specific. Additionally, findings from the persona base interview study support Technology Acceptance Model (TAM) constructs, as well as adaptability, reliability, and trust as positively correlated with perceptions of usefulness. However, despite the positive correlation, barriers and facilitators of acceptance identified in the interview suggest that perceived usefulness judgments are complex, and some questionnaire constructs were interpreted differently between participants. Thus, care should be taken when applying TAM constructs to other domains, such as robot assistance to promote older adult independence.

This thesis aims at real-time measurements of human trust, satisfaction, and frustration in human-robot teaming. Recent studies suggest that humans are inclined to have a negative attitude towards using autonomous systems. These ndings elevate the necessity of conducting research to better understand the key factors that a ect the levels of trust, satisfaction and frustration in Human-Robot Interaction (HRI). We utilized a new sequential and collaborative approach for HRI data collection that employed trust, satisfaction and frustration as primarily evaluative metrics. We also used haptic feedback through a soft actuator armband to help our human subjects control a robotic hand for grabbing or not grabbing an object during our interaction scenarios. Three experimental studies were conducted during our research of which the rst was related to the evaluation of aforementioned metrics through a collabora- tive approach between the Baxter robot and human subjects. The second experiment embodied the evaluation of a newly fabricated 3D- nger for the I-Limb robotic hand through a nuclear-waste glove. The third experiment was based on the two previous studies that focused on real-time measurements of trust, satisfaction and frustration in human-robot teaming with the addition of pressure feedback to the system through soft actuators. In the last case, human subjects had more controls over our robotic systems compared to earlier experiments leading to a more collaborative interaction and teaming. The results of these experiments illustrated that human subjects can rebuild their trust and also increase their satisfaction levels while lowering their frus- tration levels after failures or any faulty behavior. Furthermore, our analyses showed that our methods are highly e ective for collecting honest and genuine data from hu- man subjects and lays the foundation for more-involved future research in the domain of human-robot teaming. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

"When treating highly-infectious diseases such as Ebola, health workers are at high risk of infection during the doffing of Personal Protective Equipment (PPE). This is due to factors such as fatigue, hastiness, and inconsistency in training. The introduction of a semi-autonomous robot doffing assistant has the potential to increase the safety of the doffing procedure by assisting the human during high-risk sub-tasks. The addition of a robot into the procedure introduces the need to transform a purely human task into a sequence of safe and effective human-robot collaborative actions. We take advantage of the fact that the human can do the more intricate motions during the procedure. Since diseases like Ebola can spread through the mucous membranes of the eyes, ears, nose, and mouth our goal is to keep the human’s hands away from his or her face as much as possible. Thus our framework focuses on using the robot to help avoid such human risky motion. As secondary goals, we seek to also minimize the human’s effort and make the robot’s motion intuitive for the human. To address different versions and variants of PPE, we propose a way of segmenting the doffing procedure into a sequence of human and robot actions such that the robot only assists when necessary. Our framework then synthesizes assistive motions for the robot that perform parts of the tasks according to the metrics above. Our experiments on five doffing tasks suggest that the introduction of a robot assistant improves the safety of the procedure in three out of four of the high-risk doffing tasks while reducing effort in all five tasks."

The general public is being exposed to robots more often every day. This thesis focused on the advancement of research by analyzing whether or not the type of information provided by a robot determined the level of trust humans have for a robot. A study was conducted where the participants were asked to answer two different types of questions: mathematical/logical and ethical/social. The participants were divided into two different conditions: controlled and misinformed. A humanoid robot provided its own spoken answer after the participants said their answers. The participants then had the chance to select whose answers they would like to keep. During the misinformed condition, there were times when the robot purposely gave incorrect answers. The results of the study support the hypothesis that the participants were more likely to select the robot’s answers when the question type was mathematical/logical, whether the robot provided a correct or incorrect response.

It is increasingly common for people to work alongside robots in a variety of situations. When a robot is completing a task, the handler of the robot may be present. It is important to know how people interact with the robot when the handler is next to the robot. Our study focuses on whether handler’s presence can affect human’s behavior toward the robot. Our experiment targets two different scenarios (handler present and handler absent) in order to find out human’s behavior change toward the robot. Results show that in the handler present scenario, people are less willing to interact with the robot. However, when people do interact with the robot, they tend to interact with both the handler and the robot. This suggests that researchers should consider the presence of a handler when designing for human-robot interactions.