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A sensorimotor learning framework for object categorization
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: 0000-0003-0579-3372
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: 0000-0002-4266-6746
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: 0000-0003-2965-2953
2016 (English)In: IEEE Transactions on Cognitive and Developmental Systems, ISSN 2379-8920, Vol. 8, no 1, 15-25 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a framework that enables a robot to discover various object categories through interaction. The categories are described using action-effect relations, i.e. sensorimotor contingencies rather than more static shape or appearance representation. The framework provides a functionality to classify objects and the resulting categories, associating a class with a specific module. We demonstrate the performance of the framework by studying a pushing behavior in robots, encoding the sensorimotor contingencies and their predictability with Gaussian Processes. We show how entropy-based action selection can improve object classification and how functional categories emerge from the similarities of effects observed among the objects. We also show how a multidimensional action space can be realized by parameterizing pushing using both position and velocity.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. Vol. 8, no 1, 15-25 p.
Keyword [en]
sensorimotor learning, object classification, categorization, cognitive robotics, active perception, learning and adaptive system, embodiment, developmental robotics
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-172143DOI: 10.1109/TAMD.2015.2463728ISI: 000388682400003OAI: oai:DiVA.org:kth-172143DiVA: diva2:922036
Funder
Swedish Research CouncilEU, European Research Council, H2020-FETPROACT-2014 641321
Note

QC 20160422

Available from: 2016-04-21 Created: 2015-08-13 Last updated: 2017-01-04Bibliographically approved

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Maki, AtsutoKragic, Danica

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