This chapter presents Multi-Policy Decision-Making (MPDM): a novel approach to navigating in dynamic multi-agent environments. Rather than planning the trajectory of the robot explicitly, the planning process selects one of a set of closed-loop behaviors whose utility can be predicted through forward simulation that capture the complex interactions between the actions of these agents. These polices capture different high-level behavior and intentions, such as driving along a lane, turning at an intersection, or following pedestrians. We present two different scenarios where MPDM has been applied successfully: An autonomous driving environment that models vehicle behavior for both our vehicle and nearby vehicles and a social environment, where multiple agents or pedestrians configure a dynamic environment for autonomous robot navigation. We present extensive validation for MPDM on both scenarios, using simulated and real-world experiments.


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    Titel :

    MPDM: Multi-policy Decision Making From Autonomous Driving to Social Robot Navigation


    Beteiligte:
    A. G. Cunningham (Autor:in) / E. Galceran (Autor:in) / D. Mehta (Autor:in) / G. Ferrer (Autor:in) / R. M. Eustice (Autor:in) / E. Olson (Autor:in)

    Erscheinungsdatum :

    2018


    Format / Umfang :

    22 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch




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