Robot companions often default to cisnormative and heteronormative assumptions, undermining trust and inclusion for LGBTQIA+ (queer) communities. Although inclusivity is widely discussed in HRI, empirical, community-led insights into queer needs and risks remain limited. We address this gap through a mixed-methods study of how queer individuals envision affirming and trustworthy robot companions, combining an online survey of community priorities with participatory design workshops exploring lived experiences and design tensions. Although many participants felt they were not the "intended user" of current technologies, they outlined clear priorities for future robot companions, including accurate handling of names and pronouns, robust privacy and data safeguards, clear and bounded utilitarian roles, and user-controlled adaptability, while identifying risks such as misgendering, surveillance, stereotype reinforcement, and unhealthy dependence. We translate these findings into justice-oriented design requirements for robot companions serving queer and other marginalized communities.

To achieve trust and acceptance from people, robots that navigate complex human environments should be able to explain their behavior. In an online between-subjects study, this paper explores the impact of explanation detail on human understanding, trust, and comfort with robot navigators. It compares three conditions: no textual information, a simple action description, and a detailed explanation. Results indicate that while detailed explanations improve users' confidence in the robot's navigation ability, simpler descriptions are easier to understand. Participants who received detailed explanations, however, also indicated a better overall understanding of the robot's behavior and decision-making process. These findings highlight how varied levels of detail in explanations can build trust and comprehension.

It is crucial to prioritize diversity, equity, and inclusion (DEI) in the development of AI and robotics. Neglecting these factors not only exacerbates existing discrimination and biases, but also continues perpetuating them over time. Despite global awareness, urgent action is needed within the human-robot interaction (HRI) community. This workshop aims to bridge the gap by providing a platform for sharing experiences and research insights related to identifying, addressing, and integrating DEI principles in HRI. Building upon its last few iterations, this year's workshop will actively involve participants in tackling human biases which can be transferred to the robots, aiming to mitigate inequity, recognize and minimize prejudice, and promote inclusion within the field of HRI.

This paper reports on a robot controller that learns and applies a cognitively-based spatial model as it travels in challenging, real-world indoor spaces. The model not only describes indoor space, but also supports robust, model-based planning. Together with the spatial model, the controller’s reasoning framework allows it to explain and defend its decisions in accessible natural language. The novel contributions of this paper are an enhanced cognitive spatial model that facilitates successful reasoning and planning, and the ability to explain navigation choices for a complex environment. Empirical evidence is provided by simulation of a commercial robot in a large, complex, realistic world.