JESSIE: Synthesizing Social Robot Behaviors for Personalized Neurorehabilitation and Beyond

JESSIE: Synthesizing Social Robot Behaviors for Personalized Neurorehabilitation and Beyond
Alyssa Kubota, Emma I. C. Peterson, Vaishali Rajendren, Hadas Kress-Gazit, Laurel D. Riek

HRI'20: ACM/IEEE International Conference on Human-Robot Interaction
Session: Reproducibility and HRI

Abstract
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.

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Talk for the ACM/IEEE International Conference on Human-Robot Interaction 2020

SIGCHI,HRI 2020,control synthesis,healthcare robotics,dementia,robotics,neurorehabilitation,human robot interaction,