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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Veröffentlicht in:Autonomous robots Jg. 36; H. 3; S. 273 - 294
Hauptverfasser: Nguyen, Sao Mai, Oudeyer, Pierre-Yves
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.03.2014
Springer Nature B.V
Springer Verlag
Schlagworte:
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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
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ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-013-9339-y