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  • PhD Thesis Proposal - Matthew Fontaine

    Mon, Apr 25, 2022 @ 10:30 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: Matthew Fontaine

    Stefanos Nikolaidis (Chair, USC, Computer Science)
    Bistra Dilkina (USC, Computer Science)
    Gaurav Sukhatme (USC, Computer Science)
    Haipeng Luo (USC, Computer Science)
    Satyandra Kumar (USC, Mechanical Engineering)
    Julian Togelius (NYU, Computer Science)

    Title: Towards Automating the Generation of Human-Robot Interaction Scenarios

    Abstract: The human robot interaction (HRI) community currently evaluates their algorithms via hand-authored user studies. When proposing a novel algorithm, each researcher designs an experimental setup to evaluate how their new algorithm performs with human subjects. While such studies are essential to evaluating how a real human will interact with a robot, robots deployed in the real world will encounter novel scenarios not evaluated in experimental settings. To discover scenarios outside of human subjects experiments, this work proposes simulating HRI scenarios, where a scenario constitutes both an environment and simulated human agents. However, both how to explore the vast space of scenarios efficiently for diverse failures and how to generate realistic scenarios that present a feasible challenge to a human-robot team are very challenging problems. This work approaches searching the continuous space of possible scenarios as a quality diversity (QD) problem, a class of optimization problem where solving algorithms find a collection of solutions spanning a space specified by measure functions, where each solution also maximizes an objective. I present methods advancing the state-of-the-art of QD algorithms, but also a new problem setting called differentiable quality diversity (DQD) that allows for the objective and measure functions to be first order differentiable. To address the realism problem, I present methods for representing scenarios via generative models that guarantee task feasibility via mixed integer linear programming. Each of these methods is combined into an efficient scenario generation framework that tests and evaluates HRI systems. Finally, the proposed work discusses techniques for increasing the complexity of the generated scenarios and for evaluating the scenarios in real-world settings with actual end users.

    WebCast Link: https://usc.zoom.us/my/tehqin?pwd=Z2E1WVp3ais1Tng1V2NndTgvR1pQQT09

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon


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