Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback

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Abstract

Current ongoing industry revolution demands more flexible products, including robots in household environments and medium-scale factories. Such robots should be able to adapt to new conditions and environments and be programmed with ease. As an example, let us suppose that there are robot manipulators working on an industrial production line and that they need to perform a new task. If these robots were hard coded, it could take days to adapt them to the new settings, which would stop production at the factory. Robots that non-expert humans could easily program would speed up the process considerably.
Original languageEnglish
Pages (from-to)46-54
JournalIEEE Robotics and Automation Magazine
Volume27
Issue number2
DOIs
Publication statusPublished - 2020

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Artificial neural networks
  • Service robots
  • Task analysis
  • Feature extraction
  • Training
  • Computer architecture

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