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Statistical Relational Learning of Object Affordances for Robotic Manipulation
Department of Computer Science, Katholieke Universiteit Leuven, Belgium.
Department of Computer Science, Katholieke Universiteit Leuven, Belgium.
Department of Computer Science, Katholieke Universiteit Leuven, Belgium.ORCID iD: 0000-0002-6860-6303
Electrical & Computer Engineering, Instituto Superior T ́ecnico, Portugal.
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2014 (English)In: Latest Advances in Inductive Logic Programming, London: Imperial College Press, 2014, p. 95-103Chapter in book (Refereed)
Abstract [en]

We present initial results of an application of statistical relational learning using ProbLog to a robotic manipulation task modeled using affordances. Affordances encompass the action possibilities on an object, so previous works have presented models for just one object. However, in scenarios where there are multiple objects that interact, it is very useful to consider the advantages of the statistical relational learning.

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London: Imperial College Press, 2014. p. 95-103
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Computer and Information Sciences
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URN: urn:nbn:se:oru:diva-87275DOI: 10.1142/9781783265091_0012ISBN: 978-1-78326-508-4 (print)ISBN: 978-1-78326-510-7 (electronic)OAI: oai:DiVA.org:oru-87275DiVA, id: diva2:1499474
Available from: 2020-11-09 Created: 2020-11-09 Last updated: 2020-11-09Bibliographically approved

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De Raedt, Luc

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