Socially guided intrinsic motivation for robot learning of motor skills

This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our algorithmic architecture, called SGIM-D , allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and...

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Podrobná bibliografia
Vydané v:	Autonomous robots Ročník 36; číslo 3; s. 273 - 294
Hlavní autori:	Nguyen, Sao Mai, Oudeyer, Pierre-Yves
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.03.2014 Springer Nature B.V Springer Verlag
Predmet:	Algorithms Architecture Artificial Intelligence Cognitive science Computer Imaging Computer Science Control Engineering Inverse Learning Machine Learning Mechatronics Motivation Motor ability Motors Parameterization Pattern Recognition and Graphics Policies Robot arms Robot learning Robotics Robotics and Automation Robots Skills Vision Intrinsic motivation Inverse model Active learning Motor skill learning Imitation Exploration Programming by demonstration Learning from demonstration Inverse Model Intrinsic Motivation Motor Skill Learning Learning from Demonstration Programming by Demonstration Active Learning
ISSN:	0929-5593, 1573-7527
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Popis
Shrnutí:	This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our algorithmic 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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	0929-5593 1573-7527
DOI:	10.1007/s10514-013-9339-y