research
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| <source src="/public/videos/bootstrapping.ogg" type='video/ogg;codecs="theora, vorbis"'> | <source src="/public/videos/bootstrapping.ogg" type='video/ogg;codecs="theora, vorbis"'> | ||
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| - | **[[https://iis.uibk.ac.at/public/emre/research.html|Bootstrapped learning and Emergent Structuring of interdependent single and multi-object affordances]]** - Inspired from infant development, we propose a learning system for a developmental robotic system that benefits from bootstrapping, where learned simpler structures (affordances) that encode robot's interaction dynamics with the world are used in learning of complex affordances ([[https://iis.uibk.ac.at/public/emre/ICDL2014-Bootstrapping.pdf|ICDL2014-Bootstrapping]]). In order to discover the developmental order of different affordances, we use Intrinsic Motivation approach that can guide the robot to explore the actions it should execute in order to maximize the learning progress. During this learning, the robot also discovers the structure by learning and using the most distinctive object features for predicting affordances. The results show that the hierarchical structure and the development order emerged from the learning dynamics that is guided by Intrinsic Motivation mechanisms and distinctive feature selection approach ([[https://iis.uibk.ac.at/public/emre/ICDL2014-EmergentStructuring|ICDL2014-EmergentStructuring.pdf]]). | + | **[[https://iis.uibk.ac.at/public/emre/research.html|Bootstrapped learning and Emergent Structuring of interdependent single and multi-object affordances]]** - Inspired from infant development, we propose a learning system for a developmental robotic system that benefits from bootstrapping, where learned simpler structures (affordances) that encode robot's interaction dynamics with the world are used in learning of complex affordances ([[https://iis.uibk.ac.at/public/emre/papers/ICDL2014-Bootstrapping.pdf|ICDL2014-Bootstrapping]]). In order to discover the developmental order of different affordances, we use Intrinsic Motivation approach that can guide the robot to explore the actions it should execute in order to maximize the learning progress. During this learning, the robot also discovers the structure by learning and using the most distinctive object features for predicting affordances. The results show that the hierarchical structure and the development order emerged from the learning dynamics that is guided by Intrinsic Motivation mechanisms and distinctive feature selection approach ([[https://iis.uibk.ac.at/public/emre/papers/ICDL2014-EmergentStructuring.pdf|ICDL2014-EmergentStructuring.pdf]]). |
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research.txt · Last modified: 2018/09/03 19:35 (external edit)