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Title: 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.
Authors:
; ; ; ;
Award ID(s):
1915734 1935500
Publication Date:
NSF-PAR ID:
10173289
Journal Name:
Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
Page Range or eLocation-ID:
121 to 130
Sponsoring Org:
National Science Foundation
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