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Learning Deep Generative Spatial Models for Mobile Robots
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA..ORCID iD: 0000-0002-1396-0102
Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA..
2017 (English)In: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Bicchi, A Okamura, A, IEEE , 2017, p. 755-762Conference paper, Published paper (Refereed)
Abstract [en]

We propose a new probabilistic framework that allows mobile robots to autonomously learn deep, generative models of their environments that span multiple levels of abstraction. Unlike traditional approaches that combine engineered models for low-level features, geometry, and semantics, our approach leverages recent advances in Sum-Product Networks (SPNs) and deep learning to learn a single, universal model of the robot's spatial environment. Our model is fully probabilistic and generative, and represents a joint distribution over spatial information ranging from low-level geometry to semantic interpretations. Once learned, it is capable of solving a wide range of tasks: from semantic classification of places, uncertainty estimation, and novelty detection, to generation of place appearances based on semantic information and prediction of missing data in partial observations. Experiments on laser-range data from a mobile robot show that the proposed universal model obtains performance superior to state-of-the-art models fine-tuned to one specific task, such as Generative Adversarial Networks (GANs) or SVMs.

Place, publisher, year, edition, pages
IEEE , 2017. p. 755-762
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-225798ISI: 000426978201025ISBN: 978-1-5386-2682-5 OAI: oai:DiVA.org:kth-225798DiVA, id: diva2:1196285
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), SEP 24-28, 2017, Vancouver, CANADA
Funder
Swedish Research Council, 2012-4907 SKAEENet
Note

QC 20180409

Available from: 2018-04-09 Created: 2018-04-09 Last updated: 2018-04-09Bibliographically approved

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Pronobis, Andrzej

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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
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More languages
Output format
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