Journal article Open Access

Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives

João Silvério


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>João Silvério</dc:creator>
  <dc:date>2019-09-30</dc:date>
  <dc:description>During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the robotics literature. One of their most prominent features is that, in addition to extracting a mean trajectory from task demonstrations, they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new perspective on imitation learning by predicting variability, correlations and uncertainty using a single model. This rich set of information is used in combination with the fusion of optimal controllers to learn robot actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task. We showcase our approach in a painting task, where a human user and a KUKA robot collaborate to paint a wooden board. The task is divided into two sub-tasks and we show that the robot becomes compliant (hence safe) outside the training regions and executes the two sub-tasks with optimal gains otherwise</dc:description>
  <dc:identifier>https://zenodo.org/record/3676791</dc:identifier>
  <dc:identifier>10.5281/zenodo.3676791</dc:identifier>
  <dc:identifier>oai:zenodo.org:3676791</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>doi:10.5281/zenodo.3676692</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/collaborate_project</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>
51
65
views
downloads
All versions This version
Views 5131
Downloads 6534
Data volume 378.1 MB196.6 MB
Unique views 3025
Unique downloads 4930

Share

Cite as