Many clinical schools incorporate the use of real patients and standardized patients (actors), however, students often do not have a lot of contact time with these patients to practice their skills. We are investigating the use of a highly expressive android robot capable of conveying patient pathologies to augment this training.
In order to realize the vision of socially intelligent machines, we must understand the actions and interactions of people in groups. We are computationally modeling both human-human and human-robot dyadic and group interaction on dimensions such as rapport, synchrony, and mimicry, across naturalistic and laboratory contexts.
In order for robots to be capable of working along side humans, it is important they are able to understand social context, including includes situational context, social norms, social roles, and cultural conventions. We are exploring novel methods which utilize high-level context to learn an appropriateness function to inform robot actions.
Up to 400,000 people may die each year as a result of preventable medical errors, many resulting from miscommunication and low situational awareness among providers. We are exploring the use of interactive shared displays to facilitate situational awareness in collaborative tasks and procedures in medical settings.