Conference paper Restricted Access

Incremental Policy Refinement by Recursive Regression and Kinesthetic Guidance

Bojan Nemec; Mihael Simonč; Tadej Petrič; Aleš Ude


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    <subfield code="c">2020-01-31</subfield>
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    <subfield code="a">&lt;p&gt;Fast deployment of robot tasks requires appropriate&lt;br&gt;
tools that enable efficient reuse of existing robot control&lt;br&gt;
policies. Learning from Demonstration (LfD) is a popular tool&lt;br&gt;
for the intuitive generation of robot policies, but the issue of&lt;br&gt;
how to address the adaptation of existing policies has not been&lt;br&gt;
properly addressed yet. In this work, we propose an incremental&lt;br&gt;
LfD framework that efficiently solves the above-mentioned&lt;br&gt;
issue. It has been implemented and tested on a number of&lt;br&gt;
popular collaborative robots, including Franka Emika Panda,&lt;br&gt;
Universal Robot UR10, and KUKA LWR 4.&lt;/p&gt;</subfield>
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    <subfield code="u">Jožef Stefan Institute, Ljubljana, Slovenia</subfield>
    <subfield code="a">Mihael Simonč</subfield>
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    <subfield code="u">Jožef Stefan Institute, Ljubljana, Slovenia</subfield>
    <subfield code="a">Tadej Petrič</subfield>
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    <subfield code="u">Jožef Stefan Institute, Ljubljana, Slovenia</subfield>
    <subfield code="a">Aleš Ude</subfield>
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    <subfield code="u">Jožef Stefan Institute, Ljubljana, Slovenia</subfield>
    <subfield code="a">Bojan Nemec</subfield>
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    <subfield code="a">10.5281/zenodo.3632342</subfield>
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    <subfield code="a">Incremental Policy Refinement by Recursive Regression and Kinesthetic Guidance</subfield>
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    <subfield code="c">820767</subfield>
    <subfield code="a">Co-production CeLL performing Human-Robot Collaborative AssEmbly</subfield>
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