Expecting, understanding, relating, and interacting-older, middle-aged and younger adults’ perspectives on breakdown situations in human–robot dialogues - Tewari, Maitreyee; Lindgren, Helena (Frontiers in Robotics and AI, 2022)
Purpose: The purpose of this study is to explore how older, middle aged and younger adults perceive breakdown situations caused by lack of or inconsistent knowledge, sudden focus shifts, and conflicting intentions in dialogues between a human and a socially intelligent robot in a home environment, and how they perceive strategies to manage breakdown situations. Methods: Scenarios embedding dialogues on health-related topics were constructed based on activity-theoretical and argumentation frameworks. Different reasons for breakdown situations and strategies to handle these were embedded. The scenarios were recorded in a Wizard-of-Oz setup, with a human actor and a Nao robot. Twenty participants between 23 and 72 years of age viewed the recordings and participated in semi-structured interviews conducted remotely. Data were analyzed qualitatively using thematic analysis. Results: Four themes relating to breakdown situations emerged: expecting, understanding, relating, and interacting. The themes span complex human activity at different complementary levels and provide further developed understanding of breakdown situations in human–robot interaction (HRI). Older and middle-aged adults emphasized emphatic behavior and adherence to social norms, while younger adults focused on functional aspects such as gaze, response time, and length of utterances. A hierarchical taxonomy of aspects relating to breakdown situations was formed, and design implications are provided, guiding future research. Conclusion: We conclude that a socially intelligent robot agent needs strategies to 1) construct and manage its understanding related to emotions of the human, social norms, knowledge, and motive on a higher level of meaningful human activity, 2) act accordingly, for instance, adhering to transparent social roles, and 3) resolve conflicting motives, and identify reasons to prevent and manage breakdown situations at different levels of collaborative activity. Furthermore, the novel methodology to frame the dynamics of human–robot dialogues in complex activities using Activity Theory and argumentation theory was instrumental in this work and will guide the future work on tailoring the robot’s behavior.