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 architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particula...

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Bibliographic Details
Published in:arXiv.org
Main Authors: Sao Mai Nguyen, Pierre-Yves Oudeyer
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 19.04.2018
Subjects:
Algorithms
Architecture
Fishing
Machine learning
Motivation
Motor ability
Parameterization
Policies
Robot arms
Robot learning
Robots
Skills
ISSN:2331-8422
Online Access:Get full text
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Summary: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.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.1804.07269