Intelligent and Interactive Systems

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research [2014/08/01 18:47]
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research [2016/06/06 08:21]
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-**Bootstrapped learning ​through ​Emergent Structuring of interdependent single and multi-object affordances** - 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. ​We showed that a robot can benefit from a hierarchical structuring,​ where pre-learned basic affordances are used as inputs to bootstrap the learning performance of complex affordances (ICDL2014-Bootstrapping.pdf). A truly developmental system, on the other hand, should be able to self-discover such a structure, i.e. links from basic to related complex affordances,​ along with a suitable learning order. In order to discover the developmental order, 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 discovering ​and using the most distinctive object features for predicting affordances. We implemented our method in an online learning setup, and tested it in a real dataset that includes 83 objects and large number of effects created on these objects by three poke and one stack action. 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 (ICDL2014-EmergentStructuring.pdf).+<​html>​ 
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 +     <​video width="​270"​ height="​180"​ controls preload="​metadata">​ 
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 +**[[https://​iis.uibk.ac.at/​public/​emre/​research.html|From Continuous Manipulative Exploration to Symbolic Planning]]** - This work aims for bottom-up and autonomous development of symbolic planning operators from continuous interaction experience of a manipulator robot that explores the environment using its action repertoire. In the first stage, the robot explores the environment by executing actions on single objects, forms effect and object categories, and gains the ability to predict the object/​effect categories from the visual properties of the objects by learning the nonlinear and complex relations among them. In the next stage, with further interactions that involve stacking actions on pairs of objects, the system learns logical high-level rules that return a stacking-effect category given the categories of the involved objects and the discrete relations between them. Finally, these categories and rules are encoded in PDDL format, enabling symbolic planning. In the third state, the robot progressively updates the previously learned concepts and rules in order to better deal with novel situations that appear during multi-step plan executions. This way, categories of novel objects can be inferred or new categories can be formed based on previously learned rules. Our system further learns probabilistic rules that predict the action effects and the next object states. After learning, the robot was able to build stable towers in real world, exhibiting some interesting reasoning capabilities such as stacking larger objects before smaller ones, and predicting that cups remain insertable even with other objects inside. ([[https://​iis.uibk.ac.at/​public/​emre/​papers/​ICRA2015.pdf|ICRA2015.pdf]],​ [[https://​iis.uibk.ac.at/​public/​emre/​papers/​humanoids.pdf|humanoids.pdf]]). 
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 +  <div style="​border:​0;​float:​right;​margin:​0 0 0 1em">​ 
 +     <​video width="​270"​ height="​180"​ controls preload="​metadata">​ 
 +       <​source src="/​public/​videos/​bootstrapping.ogg"​ type='​video/​ogg;​codecs="​theora,​ vorbis"'>​ 
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 +        <param name="​url"​ value="/​public/​videos/​bootstrapping.ogg"/>​ 
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 +     </​video>​ </​div>​ 
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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/​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)