As robots increasingly move into homes, healthcare settings, and public environments, many are expected to support people not through single encounters, but through repeated interaction over time. In these settings, successful human–robot interaction depends not only on immediate task performance, but also on how users adapt to robotic systems, how expectations change with repeated exposure, and how interaction preferences evolve across sessions and contexts. Despite growing interest in personalization, many robotic systems still assume that user preferences can be estimated once and treated as stable, with limited understanding of how preferences develop longitudinally.

This thesis investigates longitudinal human–robot interaction by examining how user preferences, engagement, and interaction strategies change through repeated encounters with socially interactive robots. In robotic exercise support for older adults, an exploratory Wizard-of-Oz study revealed substantial variation in how participants naturally engaged with a conversational exercise robot, ranging from brief task-focused exchanges to extended social interaction. Building on these findings, a four-week longitudinal study compared two contrasting robot personalities during repeated exercise sessions: a Social Buddy Personality emphasizing companionship and conversation, and an Exercise Coach Personality emphasizing structured feedback and task-focused guidance. Participants responded positively to both personalities but valued different aspects of each, with preferences varying across individuals and shifting across sessions.

To examine whether similar temporal dynamics extend beyond exercise, this thesis also investigates repeated interaction in accessibility robotics through a longitudinal navigation study with blind and low-vision users. Across multiple weeks of navigation in public environments, participants demonstrated evolving preferences for delegation and autonomy, with assistance strategies changing according to context, familiarity, and accumulated experience. Together, these studies show that user preferences in sustained human–robot interaction are not static, but develop through repeated exposure and situational adaptation.

Motivated by these findings, this thesis proposes a multi-timescale adaptive robotic exercise coaching framework that dynamically adjusts social interaction and coaching behavior using multimodal estimates of user state, behavior, and engagement. By integrating exercise performance, conversational behavior, and interaction history, the proposed system models preference across long-term trends, session-level variation, and moment-to-moment interaction. Overall, this work contributes new insight into how temporally aware personalization can support long-term human–robot interaction in health, accessibility, and everyday assistive contexts.

The link to the document can be found here: thesis_proposal