Nathan Thomas White

I am a sixth year PhD student, studying Human-Robot Interaction as part of the People and Robots Lab at the University of Wisconsin - Madison. My advisor is Dr. Bilge Mutlu. I completed my MS degree in Computer Science at the University of Wisconsin - Madison, and my BS degree in Computer Science at the University of Minnesota - Twin Cities.

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Technology is human-centered, and thus needs to be usable by people. I am passionate about designing systems and technologies that are usable and intuitive for a wide range of potential users. My work reflects this commitment, as I have built systems and designed interactions that aim to bridge the gap between technical complexity and user understanding. While my work has focused on human-robot interaction, I have incorporated elements from human-computer interaction, design, and human behavior literature to create interfaces and interactions that simplify complex tasks, enabling users to engage with advanced technologies more effectively. By building systems that bridge the gap between technical complexity and user understanding, I aim to make advanced technologies seamlessly integrate into people's daily lives across various domains.

Check out my projects

My Projects

Here are some of the projects I've worked on.

Allocobot Human-Robot Collaboration Planner

Robo-Blocks LLM Assisted End-User Robot Programming

Robot World Beliefs Creating Consistent Robot Behaviors

CoFrame Expert-Assisted Collaborative Robot Programming

At-Home Learning Companion Robot Child-Robot Reading Companion

Early Education Math Learning Child-Robot Math Learning Interaction

Check out my publications

My Publications

Here are some of my recent publication. For a complete list of publications, please check out my Google Scholar page.

Making Informed Decisions: Supporting Cobot Integration Considering Business and Worker Preferences Venue: International Conference on Human-Robot Interaction Date: 2024-03-11 Abstract: Robots are ubiquitous in small-to-large-scale manufacturers. While collaborative robots (cobots) have significant potential in these settings due to their flexibility and ease of use, proper integration is critical to realize their full potential. Specifically, cobots need to be integrated in ways that utilize their strengths, improve manufacturing performance, and facilitate use in concert with human workers. Effective integration requires careful consideration and the knowledge of roboticists, manufacturing engineers, and business administrators. We propose an approach involving the stages of planning, analysis, development, and presentation, to inform manufacturers about cobot integration within their facilities prior to the integration process. We contextualize our approach in a case study with an SME collaborator and discuss insights learned.

End User Interfaces for Human-Robot Collaboration Venue: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction Date: 2024-03-11 Abstract: Collaborative robots (cobots) are increasingly utilized within the manufacturing industry. However, despite the promise of collaboration and easier programming when compared to traditional industrial robots, cobots introduce new interaction paradigms that require more thought about the environment and distribution of work to fully realize their collaboration capabilities. Due to these additional requirements, cobots have been found to be underutilized for their collaboration capabilities in current manufacturing. Therefore, in order to make cobots more accessible and easy to use, new systems need to be developed that support users during interaction. In this research, we propose a set of tools that target the use of cobots for multiple groups of individuals that use them, in order to better support users and simplify cobot collaboration.

Designing Parent-child-robot Interactions to Facilitate In-Home Parental Math Talk with Young Children Venue: Interaction Design and Children Date: 2023-05-04 Abstract: Parent-child interaction is critical for child development, yet parents may need guidance in some aspects of their engagement with their children. Current research on educational math robots focuses on child-robot interactions but falls short of including the parents and integrating the critical role they play in children's learning. We explore how educational robots can be designed to facilitate parent-child conversations, focusing on math talk, a predictor of later math ability in children. We prototyped capabilities for a social robot to support math talk via reading and play activities and conducted an exploratory Wizard-of-Oz in-home study for parent-child interactions facilitated by a robot. Our findings yield insights into how parents were inspired by the robot's prompts, their desired interaction styles and methods for the robot, and how they wanted to include the robot in the activities, leading to guidelines for the design of parent-child-robot interaction in educational contexts.

Designing Emotional Expressions for a Reading Companion Robot Venue: SAS Date: 2023-01-01 Abstract: Advancements in affective science and robotics have allowed researchers to consider how robots can express emotions appropriately in humanrobot interaction. Borrowing previous work from theories of nonverbal expressions of emotion, we designed 24 distinct emotional expressions for a reading companion robot–Misty II. We validated the expressions with and without social context using crowdsourcing methods. The current work aims to provide a publicly available set of emotional expressions for robots to advance research on emotional responsiveness in human-robot interaction.

Understanding Factors that Shape Children's Long Term Engagement with an In-Home Learning Companion Robot Venue: Interaction Design and Children Date: 2022-06-27 Abstract: Social robots are emerging as learning companions for children, and research shows that they facilitate the development of interest and learning even through brief interactions. However, little is known about how such technologies might support these goals in authentic environments over long-term periods of use and interaction. We designed a learning companion robot capable of supporting children reading popular-science books by expressing social and informational commentaries. We deployed the robot in homes of 14 families with children aged 10–12 for four weeks during the summer. Our analysis revealed critical factors that affected children's long-term engagement and adoption of the robot, including external factors such as vacations, family visits, and extracurricular activities; family/parental involvement; and children's individual interests. We present four in-depth cases that illustrate these factors and demonstrate their impact on children's reading experiences and discuss the implications of our findings for robot design.

CoFrame: A System for Training Novice Cabot Programmers Venue: International Conference on Human-Robot Interaction Date: 2022-03-07 Abstract: The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and challenges for those seeking to utilize their functionality. Prior research has shown that despite the capabilities afforded by cobots, there is a disconnect between those capabilities and the applications that they currently are deployed in, partially due to a lack of effective cobot-focused instruction in the field. Experts who work successfully within this collaborative domain could offer insight into the considerations and process they use to more effectively capture this cobot capability. Using an analysis of expert insights in the collaborative interaction design space, we developed a set of Expert Frames based on these insights and integrated these Expert Frames into a new training and programming system that can be used to teach novice operators to think, program, and troubleshoot in ways that experts do. We present our system and case studies that demonstrate how Expert Frames provide novice users with the ability to analyze and learn from complex cobot application scenarios.

RoboMath: Designing a Learning Companion Robot to Support Children's Numerical Skills Venue: Interaction Design and Children Date: 2021-06-24 Abstract: Children's early numerical knowledge establishes a foundation for later development of mathematics achievement and playing linear number board games is effective in improving basic numerical abilities. Besides the visuo-spatial cues provided by traditional number board games, learning companion robots can integrate multi-sensory information and offer social cues that can support children's learning experiences. We explored how young children experience sensory feedback (audio and visual) and social expressions from a robot when playing a linear number board game, “RoboMath.” We present the interaction design of the game and our investigation of children's (n = 19, aged 4) and parents' experiences under three conditions: (1) visual-only, (2) audio-visual, and (3) audiovisual-social robot interaction. We report our qualitative analysis, including the themes observed from interviews with families on their perceptions of the game and the interaction with the robot, their child's experiences, and their design recommendations.

Designing emotionally expressive social commentary to facilitate child-robot interaction Venue: Interaction Design and Children Date: 2021-06-24 Abstract: Emotion expression in human-robot interaction has been widely explored, however little is known about how such expressions should be coupled with feelings and opinions expressed by a social robot. We explored how 12 children experienced emotionally expressive social commentaries from a reading companion robot across five interaction styles that differed in their non-verbal emotional expressiveness and opinionated conversational styles (neutral, divergent, or convergent opinions). We found that, while the robot’s opinions and non-verbal emotion expressions affected children’s experiences with the robot, the speech content of the commentaries was the more prominent factor in their experience. Additionally, children differed in their perceptions of social commentary: while some expressed a sense of connection-making with the robot’s self-disclosure commentaries, others felt distracted by them or felt like the robot was off-topic. We recommend designers pay particular attention to the robot’s speech content and consider children’s individual differences in designing emotional and opinionated speech.

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