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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        <foaf:name>Tadej Petrič</foaf:name>
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        <foaf:name>Aleš Ude</foaf:name>
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    <dct:title>Incremental Policy Refinement by Recursive Regression and Kinesthetic Guidance</dct:title>
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    <dct:description>&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;</dct:description>
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