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Learning Predictive State Representations for planning
Computer Vision and Active Perception Lab, Centre for Autonomous Systems, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden. (AASS)ORCID iD: 0000-0003-3958-6179
Computer Vision and Active Perception Lab, Centre for Autonomous Systems, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden.
Computer Vision and Active Perception Lab, Centre for Autonomous Systems, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden.
2015 (English)In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Press, 2015, p. 3427-3434Conference paper, Published paper (Refereed)
Abstract [en]

Predictive State Representations (PSRs) allow modeling of dynamical systems directly in observables and without relying on latent variable representations. A problem that arises from learning PSRs is that it is often hard to attribute semantic meaning to the learned representation. This makes generalization and planning in PSRs challenging. In this paper, we extend PSRs and introduce the notion of PSRs that include prior information (P-PSRs) to learn representations which are suitable for planning and interpretation. By learning a low-dimensional embedding of test features we map belief points of similar semantic to the same region of a subspace. This facilitates better generalization for planning and semantical interpretation of the learned representation. In specific, we show how to overcome the training sample bias and introduce feature selection such that the resulting representation emphasizes observables related to the planning task. We show that our P-PSRs result in qualitatively meaningful representations and present quantitative results that indicate improved suitability for planning.

Place, publisher, year, edition, pages
IEEE Press, 2015. p. 3427-3434
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Computer Sciences Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:oru:diva-71561DOI: 10.1109/IROS.2015.7353855ISI: 000371885403089Scopus ID: 2-s2.0-84958177858OAI: oai:DiVA.org:oru-71561DiVA, id: diva2:1280207
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, September 28 - October 2, 2015
Available from: 2019-01-18 Created: 2019-01-18 Last updated: 2025-02-01Bibliographically approved

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Stork, Johannes Andreas

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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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