On the potential for human-centered, cognitively inspired AI to bridge the gap between optimism and reality for autonomous robotics in healthcare: a respectful critique

Abstract

Historically, the fields of computer science, cognitive science, and neuroscience have been tightly linked. To date, this collaboration has yielded major advances in how the brain and mind are understood, as well as the ways in which artificial minds can be constructed to serve as new collaborators to humans. Yet there are still significant gaps between the capabilities of state-of-the-art autonomous robots and the expectations developed by real users who are now encountering autonomous robots on the job. We present our views as well as a case study of our evaluation of two autonomous robots intended to aid nurses within hospital settings: Moxi and TUG. Both cobots were originally considered for procurement by our collaborating healthcare system,UHS, at which time our team began the process of trying to systematically vet each option to aid in the decision making process. What we found was a lack of evidence on either platform in academic literature, which led us to analyze user comments on social media. In order to improve the fit of autonomous robots into human environments, research must be conducted and evidence must be shared, and the Human Factors community can aid in this effort

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