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   <dc:title>Personalization framework for adaptive robotic feeding assistance</dc:title>
   <dc:creator>Canal Camprodon, Gerard</dc:creator>
   <dc:creator>Alenyà Ribas, Guillem</dc:creator>
   <dc:creator>Torras, Carme</dc:creator>
   <dc:subject>Àrees temàtiques de la UPC::Informàtica::Robòtica</dc:subject>
   <dc:subject>Feeding</dc:subject>
   <dc:subject>Personnel training</dc:subject>
   <dc:subject>Robotics</dc:subject>
   <dc:subject>Robots</dc:subject>
   <dc:subject>Assistive robotics</dc:subject>
   <dc:subject>Automatic feeding</dc:subject>
   <dc:subject>Expert users</dc:subject>
   <dc:subject>Personalizations</dc:subject>
   <dc:subject>Training strategy</dc:subject>
   <dc:subject>Classificació INSPEC::Automation::Robots</dc:subject>
   <dcterms:abstract>The final publication is available at link.springer.com</dcterms:abstract>
   <dcterms:abstract>The deployment of robots at home must involve robots with pre-defined skills and the capability of&#xd;
personalizing their behavior by non-expert users. A framework to tackle this personalization is presented and applied&#xd;
to an automatic feeding task. The personalization involves the caregiver providing several examples of feeding using&#xd;
Learning-by- Demostration, and a ProMP formalism to compute an overall trajectory and the variance along the path.&#xd;
Experiments show the validity of the approach in generating different feeding motions to adapt to user’s preferences,&#xd;
automatically extracting the relevant task parameters. The importance of the nature of the demonstrations is also&#xd;
assessed, and two training strategies are compared. © Springer International Publishing AG 2016.</dcterms:abstract>
   <dcterms:abstract>Peer Reviewed</dcterms:abstract>
   <dcterms:abstract>Postprint (author's final draft)</dcterms:abstract>
   <dcterms:issued>2016</dcterms:issued>
   <dc:type>Conference report</dc:type>
   <dc:relation>http://link.springer.com/chapter/10.1007%2F978-3-319-47437-3_3</dc:relation>
   <dc:rights>Open Access</dc:rights>
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