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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    Title :

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


    Contributors:
    A. G. Cunningham (author) / E. Galceran (author) / D. Mehta (author) / G. Ferrer (author) / R. M. Eustice (author) / E. Olson (author)

    Publication date :

    2018


    Size :

    22 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




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