Search for: All records

Robotics is an industry that has disrupted the health field in different areas, including older adult care. Robotic companions for older adults have proved to produce many benefits, including decreasing loneliness, anxiety, stress, depression, agitation, and contributing to the improvement of social relationships, to mention a few. Currently, there are different options in the market of robotic pets for the older adult population. Among them is the Joy for All line of products. In December of 2015, Hasbro released the Joy for All - Robotic Cat to the market. While the product has been successfully adopted by the older adult population, there are opportunities to improve the nature of advanced human-robot interaction. The class, Technologies to Extend Life, taught in the Master of Design program at the University of Cincinnati, focused on designing the next generation of Joy for All robotic intelligence that could provide psycho-social support for older adults. With this project, the class had two primary objectives: 1) Understand the real challenges older adults face in activities of daily living; and 2) Develop innovative hardware and software tools that can meet some of the identified daily challenges. The class had a co-design approach in the many phases that included different participants (older adults and caregivers), utilizing a variety of tools to assess, analyze, develop, design, implement, and evaluate the interventions with older adults and communities. The significance of this project is to use a participatory approach to assess and analyze the challenges older adults face in activities of daily living that robotic companions may help alleviate, thus having an empathic perspective to the creation of assistive technologies. 

Human-robot trust is crucial to successful human-robot interaction. We conducted a study with 798 participants distributed across 32 conditions using four dimensions of human-robot trust (reliable, capable, ethical, sincere) identified by the Multi-Dimensional-Measure of Trust (MDMT). We tested whether these dimensions can differentially capture gains and losses in human-robot trust across robot roles and contexts. Using a 4 scenario × 4 trust dimension × 2 change direction between-subjects design, we found the behavior change manipulation effective for each of the four subscales. However, the pattern of results best supported a two-dimensional conception of trust, with reliable-capable and ethical-sincere as the major constituents. 

Human behavior is frequently guided by social and moral norms, and no human community can exist without norms. Robots that enter human societies must therefore behave in norm-conforming ways as well. However, currently there is no solid cognitive or computational model available of how human norms are represented, activated, and learned. We provide a conceptual and psychological analysis of key properties of human norms and identify the demands these properties put on any artificial agent that incorporates norms—demands on the format of norm representations, their structured organization, and their learning algorithms.