Programming social robots is challenging for novice robot programmers due to required expertise in planning, interaction design, and programming. While large language models (LLMs) hold significant promise through code generation from natural-language descriptions, they can obscure critical elements of programming and supplant designer intent, eventually resulting in over-reliance instead of developing programming skills. In this paper, we explore how LLM-based social-robot-programming tools can support novice robot programmers through a Research through Design (RtD) process. We designed and prototyped Robo-Blocks, a block-based programming environment that leverages LLMs to offer novice robot programmers generative scaffolding through structured narratives that connect high-level ideas to executable robot behaviors. Through deployment with novices, we discovered emerging user personas and usage patterns for generative scaffolding and showed how this scaffolding shapes end-user design and programming strategies. We present design insights for the effective use of generative scaffolding and its integration into the practice of social-robot programming.

End-user robot programming grants users the flexibility to re-task robots in situ, yet it remains challenging for novices due to the need for specialized robotics knowledge. Large Language Models (LLMs) hold the potential to lower the barrier to robot programming by enabling task specification through natural language. However, current LLM-based approaches generate opaque, "black-box" code that is difficult to verify or debug, creating tangible safety and reliability risks in physical systems. We present RoboCritics, an approach that augments LLM-based robot programming with expert-informed motion-level critics. These critics encode robotics expertise to analyze motion-level execution traces for issues such as joint speed violations, collisions, and unsafe end-effector poses. When violations are detected, critics surface transparent feedback and offer one-click fixes that forward structured messages back to the LLM, enabling iterative refinement while keeping users in the loop. We instantiated RoboCritics in a web-based interface connected to a UR3e robot and evaluated it in a between-subjects user study (n=18). Compared to a baseline LLM interface, RoboCritics reduced safety violations, improved execution quality, and shaped how participants verified and refined their programs. Our findings demonstrate that RoboCritics enables more reliable and user-centered end-to-end robot programming with LLMs.

Roboticists are continually improving the quality of social robot behaviors and interactions with humans. This is a major goal of the field of social robotics, which seeks to create socially competent robots and improve their overall acceptance. Toward this effort, we propose the utilization of primal world beliefs (i.e., beliefs about the character of the world) to design behaviors that are relatable, intuitive, and based on an internal motivation. However, it is not yet clear whether humans can reliably discern these world beliefs in robots. In this work, we explore whether primals can serve as a novel framework to inform the design of robot personality and attempt to understand whether and how humans perceive primals within robots. Through two large online user studies ( \(n=300\) ; \(n=360\) ), we show that (1) participants are broadly able to discern intended primals in robots, (2) certain participant and robot primals predict participant perception of the robots, and (3) similarity between human and robot primals predicts improved perception of robots.

Intergenerational co-creation using technology between grandparents and grandchildren can be challenging due to differences in technological familiarity.AI has emerged as a promising tool to support co-creative activities, offering flexibility and creative assistance, but its role in facilitating intergenerational connection remains underexplored.In this study, we conducted a user study with 29 grandparent-grandchild groups engaged in AI-supported story creation to examine how AI-assisted co-creation can foster meaningful intergenerational bonds.Our findings show that grandchildren managed the technical aspects, while grandparents contributed creative ideas and guided the storytelling.AI played a key role in structuring the activity, facilitating brainstorming, enhancing storytelling, and balancing the contributions of both generations.The process fostered mutual appreciation, with each generation recognizing the strengths of the other, leading to an engaging and cohesive co-creation process.We offer design implications for integrating AI into intergenerational co-creative activities, emphasizing how AI can enhance connection across skill levels and technological familiarity.

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.

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.

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.

The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and chal-lenges 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.

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) audio-visual-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.

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.