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Simultaneous control and recognition of demonstrated behavior
Umeå universitet, Institutionen för datavetenskap.ORCID iD: 0000-0002-6568-9342
Umeå universitet, Institutionen för datavetenskap.
Umeå universitet, Institutionen för datavetenskap.
2011 (English)Report (Other academic)
Resource type
Text
Abstract [en]

A method for Learning from Demonstration (LFD) is presented and evaluated on a simulated Robosoft Kompai robot. The presented algorithm, called Predictive Sequence Learning (PSL), builds fuzzy rules describing temporal relations between sensory-motor events recorded while a human operator is tele-operating the robot. The generated rule base can be used to control the robot and to predict expected sensor events in response to executed actions. The rule base can be trained under different contexts, represented as fuzzy sets. In the present work, contexts are used to represent different behaviors. Several behaviors can in this way be stored in the same rule base and partly share information. The context that best matches present circumstances can be identified using the predictive model and the robot can in this way automatically identify the most suitable behavior for precent circumstances. The performance of PSL as a method for LFD is evaluated with, and without, contextual information. The results indicate that PSL without contexts can learn and reproduce simple behaviors. The system also successfully identifies the most suitable context in almost all test cases. The robot's ability to reproduce more complex behaviors, with partly overlapping and conflicting information, significantly increases with the use of contexts. The results support a further development of PSL as a component of a dynamic hierarchical system performing control and predictions on several levels of abstraction. 

Place, publisher, year, edition, pages
Umeå: Umeå University, Department of Computing Science , 2011. , 22 p.
Series
Report / UMINF, ISSN 0348-0542 ; 15
Keyword [en]
Behavior Recognition, Context Dependent, Fuzzy Logic, Learning and Adaptive Systems, Learning from Demonstration
National Category
Robotics
Identifiers
URN: urn:nbn:se:his:diva-12147OAI: oai:DiVA.org:his-12147DiVA: diva2:1076492
Available from: 2017-02-22 Created: 2017-02-22 Last updated: 2017-03-17Bibliographically approved

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Billing, ErikHellström, ThomasJanlert, Lars Erik
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf