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Contextual Modeling with Labeled Multi-LDA
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. (CVAP)ORCID iD: 0000-0002-8640-9370
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-5750-9655
2013 (English)In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE , 2013, 2264-2271 p.Conference paper, Published paper (Refereed)
Abstract [en]

Learning about activities and object affordances from human demonstration are important cognitive capabilities for robots functioning in human environments, for example, being able to classify objects and knowing how to grasp them for different tasks. To achieve such capabilities, we propose a Labeled Multi-modal Latent Dirichlet Allocation (LM-LDA), which is a generative classifier trained with two different data cues, for instance, one cue can be traditional visual observation and another cue can be contextual information. The novel aspects of the LM-LDA classifier, compared to other methods for encoding contextual information are that, I) even with only one of the cues present at execution time, the classification will be better than single cue classification since cue correlations are encoded in the model, II) one of the cues (e.g., common grasps for the observed object class) can be inferred from the other cue (e.g., the appearance of the observed object). This makes the method suitable for robot online and transfer learning; a capability highly desirable in cognitive robotic applications. Our experiments show a clear improvement for classification and a reasonable inference of the missing data.

Place, publisher, year, edition, pages
IEEE , 2013. 2264-2271 p.
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Keyword [en]
LDA, Topic Model, Contextual Modeling
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-134127DOI: 10.1109/IROS.2013.6696673ISI: 000331367402063Scopus ID: 2-s2.0-84893758693ISBN: 978-146736358-7 (print)OAI: oai:DiVA.org:kth-134127DiVA: diva2:664981
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 3-8, 2013 at Tokyo Big Sight, Japan
Note

QC 20131217

Available from: 2013-11-18 Created: 2013-11-18 Last updated: 2014-04-10Bibliographically approved

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Contextual Modeling with Labeled Multi-LDA(1520 kB)154 downloads
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Zhang, ChengKjellström, Hedvig

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