JESSIE: Synthesizing Social Robot Behaviors for Personalized Neurorehabilitation and Beyond

JESSIE is a robotic system that enables novice programmers to program social robots by expressing high-level specifications. We employ control synthesis with a tangible front-end to allow users to define complex behavior for which we automatically generate control code. We demonstrate JESSIE in the context of enabling clinicians to create personalized treatments for people with mild cognitive impairment (MCI) on a Kuri robot, in little time and without error. We evaluated JESSIE with neuropsychologists who reported high usability and learnability. They gave suggestions for improvement, including increased support for personalization, multi-party programming, collaborative goal setting, and re-tasking robot role post-deployment, which each raise technical and sociotechnical issues in HRI. We exhibit JESSIE’s reproducibility by replicating a clinician-created program on a TurtleBot 2. As an open-source means of accessing control synthesis, JESSIE supports reproducibility, scalability, and accessibility of personalized robots for HRI.CCS CONCEPTS •Computer systems organization →Robotic control;• Applied computing →Health informatics. ACM Reference Format: Alyssa Kubota
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, Emma I. C. Peterson
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, Vaishali Rajendren
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, Hadas KressGazit
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, Laurel D. Riek
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. 2020. JESSIE: Synthesizing Social Robot Behaviors for Personalized Neurorehabilitation and Beyond. In Proceedings of the 2020 ACMlIEEE International Conference on Human-Robot Interaction (HRI’20), March 23-26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3319502.3374836