Adaptive Affordance Design for Social Robots: Tailoring to Role-Specific Preferences

As social robots become integral to diverse interaction scenarios, their design must effectively engage users emotionally while accurately conveying their functional capabilities through coherent design. While previous research in adaptive human-robot interaction has predominantly focused on enabling robots to learn and adapt to users' dynamic behavior, less attention has been paid to robots' affordances. This study addresses this gap by investigating how users' preferences for a social robot's affordances, such as look and voice, vary across different social roles and scenarios. Using the Stereotype Content Model's dimensions of warmth and competence, we conducted a quantitative survey with Likert-scale measures to assess preferences for affordance designs in general and context-dependent use cases. Our results show a medium to low preference for humanlike affordances when the robot's intended use is unspecified. While human likeness positively correlates with perceived warmth and competence, these social perceptions do not significantly drive preferences for humanlike features. Instead, preferences are primarily influenced by robots' intended situational roles, which differ from the stereotypical occupational roles in human society. These results highlight the importance of context-specific adaptive affordance design, which should focus on aligning robot characteristics with contextual expectations of social attributes.