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Formalising learning from demonstration
Umeå universitet, Institutionen för datavetenskap.ORCID iD: 0000-0002-6568-9342
Umeå universitet, Institutionen för datavetenskap.
2008 (English)Report (Other academic)
Resource type
Abstract [en]

The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. Inspired by the work on planning and actuation by LaValle, common 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 the mappings between some of these spaces. Finally, behavior primitives are introduced as one example of useful bias in the learning process, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination.

Place, publisher, year, edition, pages
Umeå: Department of Computing Science, Umeå University , 2008. , 10 p.
Report / UMINF, ISSN 0348-0542 ; 08:10
Keyword [en]
Action selection, behavior, bias, generalization, goal, learning from demonstration, robot learning, segmentation
National Category
Human Computer Interaction
URN: urn:nbn:se:his:diva-12144OAI: diva2:1076495
Available from: 2017-02-22 Created: 2017-02-22 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

fulltext(132 kB)