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Data-driven Policy Transfer with Imprecise Perception Simulation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00322428" target="_blank" >RIV/68407700:21230/18:00322428 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21730/18:00322428

  • Result on the web

    <a href="http://cmp.felk.cvut.cz/pub/cmp/articles/svoboda/Pecka-Zimermann-Petrlik-Svoboda-IEEE-RAL-2018.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/svoboda/Pecka-Zimermann-Petrlik-Svoboda-IEEE-RAL-2018.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/LRA.2018.2857927" target="_blank" >10.1109/LRA.2018.2857927</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Data-driven Policy Transfer with Imprecise Perception Simulation

  • Original language description

    This paper presents a complete pipeline for learning continuous motion control policies for a mobile robot when only a non-differentiable physics simulator of robot-terrain interactions is available. The multi-modal state estimation of the robot is also complex and difficult to simulate, so we simultaneously learn a generative model which refines simulator outputs. We propose a coarse-to-fine learning paradigm, where the coarse motion planning is alternated with guided learning and policy transfer to the real robot. The policy is jointly optimized with the generative model. We evaluate the method on a real-world platform.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database

  • CEP classification

  • OECD FORD branch

    20204 - Robotics and automatic control

Result continuities

  • Project

    <a href="/en/project/GA17-08842S" target="_blank" >GA17-08842S: Robust motion planning and control on rough unstructured terrain</a><br>

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    IEEE Robotics and Automation Letters

  • ISSN

    2377-3766

  • e-ISSN

    2377-3766

  • Volume of the periodical

    3

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    6

  • Pages from-to

    3916-3921

  • UT code for WoS article

    000441444700031

  • EID of the result in the Scopus database

    2-s2.0-85063308179