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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 (engelsk)Rapport (Annet vitenskapelig)
Resurstyp
Text
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.

sted, utgiver, år, opplag, sider
Umeå: Department of Computing Science, Umeå University , 2008. , s. 10
Serie
Report / UMINF, ISSN 0348-0542 ; 08:10
Emneord [en]
Action selection, behavior, bias, generalization, goal, learning from demonstration, robot learning, segmentation
HSV kategori
Identifikatorer
URN: urn:nbn:se:his:diva-12144OAI: oai:DiVA.org:his-12144DiVA, id: diva2:1076495
Tilgjengelig fra: 2017-02-22 Laget: 2017-02-22 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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