Autonomy and independent navigation are vital to daily life but remain challenging for individuals with blindness. Robotic systems can enhance mobility and confidence by providing intelligent navigation assistance. However, fully autonomous systems may reduce users’ sense of control, even when they wish to remain actively involved. Although collaboration between user and robot has been recognized as important, little is known about how perceptions of this relationship change with repeated use. We present a repeated exposure study with six blind participants who interacted with a navigation-assistive robot in a real-world museum. Participants completed tasks such as navigating crowds, approaching lines, and encountering obstacles. Findings show that participants refined their strategies over time, developing clearer preferences about when to rely on the robot versus act independently. This work provides insights into how strategies and preferences evolve with repeated interaction and offers design implications for robots that adapt to user needs over time.

Exercise supports healthy aging, but motivation often declines with age, increasing demand on therapists and coaches. We present a conversational robotic exercise coach that promotes engagement and assesses motivation through dialogue. In a WoZ study with ten adults aged 59 and above, participants showed varied interaction styles; even those with low motivation rated sessions positively, suggesting such agents can enhance exercise enjoyment. We identify three design needs for autonomous coaches: rephrasing for clarity, conversation beyond exercise, and adaptable speech delivery.

Different people respond to feedback and guidance in different ways, and their preferences may change based on their mood, tiredness, etc. We present a robot exercise coach that provides verbal and nonverbal feedback in two different styles: firm and encouraging. We collect a dataset of people experiencing both feedback styles and show that the style that someone performs best with may not be the one they have the best subjective experience with or be the one that they state they prefer. To account for this, we present a contextual bandit approach that enables the robot coach to learn the best style to use over time to improve the human's performance, and show that this approach performs quite well in expectation on the real human data.

While there is no replacement for the learned expertise, devotion, and social benefits of a guide dog, there are scenarios in which a robot navigation assistant could be helpful for individuals with blindness or low vision (BLV). This case study investigated the potential for an industrial quadruped robot to perform guided navigation tasks based on a co-design model. The research was informed by a guide dog handler with over 30 years of experience of non-visual navigation. In order to communicate spatial information between the human-robot team, two interface prototypes were created and pilot tested: a voice-based app and a flexible, responsive guide handle. The pilot user study consisted of sighted participants and our BLV co-designer, who completed simple navigation tasks and a post-study survey about the prototype functionality and their trust in the robot. All participants successfully completed the navigation tasks and demonstrated that the interface prototypes were able to pass spatial information between the human and the robot. Findings of this exploratory study will help to inform human-robot teaming and collaboration. Future work will include expanding the voice-based app to allow the robot to directly communicate obstacles to the handler, adding haptic navigation signals to the handle design, and expanding the user study to include a larger sample of experienced guide dog handlers.