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  • PhD Defense - Caitlyn Clabaugh

    Mon, Oct 29, 2018 @ 01:00 PM - 03:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    Title: Human-Robot Learning: Computational Personalization For Socially Assistive Robotics

    Time: 01:00 PM on Monday, October 29th, 2018
    Location: RTH 406

    Ph.D. Candidate: Caitlyn Clabaugh

    Prof. Maja Matarić
    Prof. Gaurav Sukhatme
    Prof. Gisele Ragusa


    Socially assistive robotics (SAR) seeks to support human care and development through long-term, socially co-present interaction, supplementing the efforts of clinicians and educators. One primary objective in SAR is the personalization or tailoring of interaction to meet the unique and evolving abilities, preferences, and needs of individuals. This dissertation proposes a theoretical framework for human-robot learning (HRL) to enable computational personalization.

    HRL is formalized as a hierarchical decision-making problem, wherein the robot selects actions to maximize an individual's psychosocial state and progress toward some assistive goal. The framework groups SAR actions into abstract categories based on theories from psychology and linguistics. Actions within each category are selected by a local policy or controller. The controllers themselves are activated by a meta-controller, an overarching heuristic or algorithm that controls the flow of the intervention. In this way, the framework hierarchically decomposes the large state-action spaces of SAR into more tractable subspaces for computational personalization.

    The proposed framework was instantiated as an individualized SAR intervention for early childhood math. To validate the framework and its instantiation, a short-term study was conducted with typically developing children in a general preschool classroom. The data collected informed iterative design and computational personalization, culminating in a long-term, in-home SAR intervention for children with autism spectrum disorder (ASD). In this context, individualization was framed as a reinforcement learning problem, adapting the SAR's instruction and feedback to each child over many interactions.

    The fully autonomous SAR system was deployed for month-long interventions in the homes of ten children with ASD. The single-subject study found that the SAR system successfully individualized its instruction and feedback to each child participant over time. Additionally, all participants showed improvement in their mathematics skills and long-term retention of intervention content, demonstrating the quality of the individualization. This research designed, developed, and deployed a novel, fully autonomous, long-term, in-home, individualized SAR intervention for children with diverse needs. As a broader contribution, this dissertation formalizes the problem of HRL and offers a validated, theoretical framework to inform future research at the intersection of artificial intelligence and human-machine interaction.

    Location: 406

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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