January 31, 2020 Conference paper Restricted Access

Bojan Nemec; Mihael Simonč; Tadej Petrič; Aleš Ude

DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3632342</identifier>
  <creators>
    <creator>
      <creatorName>Bojan Nemec</creatorName>
      <affiliation>Jožef Stefan Institute, Ljubljana, Slovenia</affiliation>
    </creator>
    <creator>
      <creatorName>Mihael Simonč</creatorName>
      <affiliation>Jožef Stefan Institute, Ljubljana, Slovenia</affiliation>
    </creator>
    <creator>
      <creatorName>Tadej Petrič</creatorName>
      <affiliation>Jožef Stefan Institute, Ljubljana, Slovenia</affiliation>
    </creator>
    <creator>
      <creatorName>Aleš Ude</creatorName>
      <affiliation>Jožef Stefan Institute, Ljubljana, Slovenia</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Incremental Policy Refinement by Recursive Regression and Kinesthetic Guidance</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-01-31</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3632342</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3632341</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/collaborate_project</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/restrictedAccess">Restricted Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/820767/">820767</awardNumber>
      <awardTitle>Co-production CeLL performing Human-Robot Collaborative AssEmbly</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>