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A formalism for lea...
A formalism for learning from demonstration
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- Billing, Erik, 1981- (author)
- Umeå universitet,Institutionen för datavetenskap,Department of Computing Science, Umeå University, Umeå, Sweden
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- Hellström, Thomas, 1956- (author)
- Umeå universitet,Institutionen för datavetenskap,Department of Computing Science, Umeå University, Umeå, Sweden
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(creator_code:org_t)
- 2010-03-31
- 2010
- English.
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In: Paladyn - Journal of Behavioral Robotics. - : De Gruyter Open. - 2080-9778 .- 2081-4836. ; 1:1, s. 1-13
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Människa-datorinteraktion (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Human Computer Interaction (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Learning from demonstration
- ambiguities
- behavior
- bias
- generalization
- robot learning
- Cognitive science
- Kognitionsvetenskap
- Computer science
- Datavetenskap
- data- och systemvetenskap
- computer and systems sciences
Publication and Content Type
- ref (subject category)
- art (subject category)
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