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A formalism for learning from demonstration
Umeå universitet, Institutionen för datavetenskap.ORCID iD: 0000-0002-6568-9342
Umeå universitet, Institutionen för datavetenskap.
2010 (English)In: Paladyn Journal of Behavioral Robotics, ISSN 2080-9778, Vol. 1, no 1, 1-13 p.Article in journal (Refereed) PublishedText
Abstract [en]

The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. LFD-related concepts like goal, generalization, and repetition are here defined, analyzed, and put into context. Robot behaviors are described in terms of trajectories through information spaces and learning is formulated as mappings between some of these spaces. Finally, behavior primitives are introduced as one example of good bias in learning, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination. The formalism is exemplified through a sequence learning task where a robot equipped with a gripper arm is to move objects to specific areas. The introduced concepts are illustrated with special focus on how bias of various kinds can be used to enable learning from a single demonstration, and how ambiguities in demonstrations can be identified and handled.

Place, publisher, year, edition, pages
Versita , 2010. Vol. 1, no 1, 1-13 p.
Keyword [en]
Learning from demonstration, ambiguities, behavior, bias, generalization, robot learning
National Category
Human Computer Interaction Computer Science
Research subject
Computer and Information Science
Identifiers
URN: urn:nbn:se:his:diva-12143DOI: 10.2478/s13230-010-0001-5OAI: oai:DiVA.org:his-12143DiVA: diva2:1076498
Note

co-published with Springer-Verlag GmbH; Published online: 31 March 2010

Available from: 2017-02-22 Created: 2017-02-22 Last updated: 2017-02-27

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Billing, ErikHellström, Thomas
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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
  • rtf