Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills

Sao Mai Nguyen 1 Pierre-Yves Oudeyer 1
1 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, ENSTA ParisTech U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line , we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.
Contributeur : Sao Mai Nguyen <>
Soumis le : lundi 3 mars 2014 - 21:17:32
Dernière modification le : jeudi 5 janvier 2017 - 01:53:17
Document(s) archivé(s) le : mardi 3 juin 2014 - 10:56:24


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Sao Mai Nguyen, Pierre-Yves Oudeyer. Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills. Autonomous Robots, Springer Verlag, 2014, 36 (3), pp.273-294. <>. <10.1007/s10514-013-9339-y>. <hal-00936938v2>



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