Socially assistive robots (SARs) are becoming more prevalent in everyday life, emphasizing the need to make them socially acceptable and aligned with users’ expectations. Robots’ appearance impacts users’ behaviors and attitudes toward them. Therefore, product designers choose visual qualities to give the robot a character and to imply its functionality and personality. In this work, we sought to investigate the effect of cultural differences on Israeli and German designers’ perceptions of SARs’ roles and appearance in four different contexts: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. The key insight is that although Israeli and German designers share similar perceptions of visual qualities for most of the robotics roles, we found differences in the perception of the COVID-19 officer robot’s role and, by that, its most suitable visual design. This work indicates that context and culture play a role in users’ perceptions and expectations; therefore, they should be taken into account when designing new SARs for diverse contexts.@en

With the increasing adoption of Artificial intelligence (AI) as a crucial component of business strategy, establishing trust between humans and AI teammates remains a key issue. The project “We are in this together” highlights current theories on trust in Human-AI teams (HAIT) and proposes a research model that integrates insights from Industrial and Organizational Psychology, Human Factors Engineering, Human-Computer Interaction, and Computer Science. The proposed model suggests that in HAIT, trust involves multiple actors and is critical for team success. We present three main propositions for understanding trust in HAIT collaboration, focused on trustworthiness and trustworthiness reactions in interpersonal relationships between humans and AI teammates. We further suggest that individual, technological, and environmental factors impact trust relationships in HAIT. The project aims to contribute in developing effective HAIT by proposing a research model of trust in HAIT.

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Robots’ visual qualities (VQs) impact people’s perception of their characteristics and affect users’ behaviors and attitudes toward the robot. Recent years point toward a growing need for Socially Assistive Robots (SARs) in various contexts and functions, interacting with various users. Since SAR types have functional differences, the user experience must vary by the context of use, functionality, user characteristics, and environmental conditions. Still, SAR manufacturers often design and deploy the same robotic embodiment for diverse contexts. We argue that the visual design of SARs requires a more scientific approach considering their multiple evolving roles in future society. In this work, we define four contextual layers: the domain in which the SAR exists, the physical environment, its intended users, and the robot’s role. Via an online questionnaire, we collected potential users’ expectations regarding the desired characteristics and visual qualities of four different SARs: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. Results indicated that users’ expectations differ regarding the robot’s desired characteristics and the anticipated visual qualities for each context and use case.

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Relationships are crucial for human existence. People form relationships with other humans, pets, objects, and places. We argue that the nature of human-SAR (Socially assistive robot) relationship changes by context of use and interaction level. Therefore, context and interaction must be incorporated into the design requirements. Earlier studies identified design-related preference differences among users, depending on their personal characteristics and on their role in specific contexts. To align the robotic visual qualities (VQ) with users' expectations, we propose two human-SAR relationship models: context-based model- Situational based model and interaction-based model- Dynamic based model. Together with the VQ's evaluation, these models aim to guide industrial designers in the design process of new SARs. An evaluation method and preliminary findings are presented.

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