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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  <dc:creator>Bojan Nemec</dc:creator>
  <dc:creator>Mihael Simonč</dc:creator>
  <dc:creator>Tadej Petrič</dc:creator>
  <dc:creator>Aleš Ude</dc:creator>
  <dc:date>2020-01-31</dc:date>
  <dc:description>Fast deployment of robot tasks requires appropriate
tools that enable efficient reuse of existing robot control
policies. Learning from Demonstration (LfD) is a popular tool
for the intuitive generation of robot policies, but the issue of
how to address the adaptation of existing policies has not been
properly addressed yet. In this work, we propose an incremental
LfD framework that efficiently solves the above-mentioned
issue. It has been implemented and tested on a number of
popular collaborative robots, including Franka Emika Panda,
Universal Robot UR10, and KUKA LWR 4.</dc:description>
  <dc:identifier>https://zenodo.org/record/3632342</dc:identifier>
  <dc:identifier>10.5281/zenodo.3632342</dc:identifier>
  <dc:identifier>oai:zenodo.org:3632342</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/820767/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3632341</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/collaborate_project</dc:relation>
  <dc:rights>info:eu-repo/semantics/restrictedAccess</dc:rights>
  <dc:title>Incremental Policy Refinement by Recursive Regression and Kinesthetic Guidance</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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