Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills

TL;DR

This paper introduces SGIM-D, a robot learning architecture that combines active intrinsic motivation with imitation learning to efficiently acquire complex motor skills in high-dimensional spaces.

Contribution

The paper presents a novel integration of social learning and intrinsic motivation within an active goal babbling framework for robot motor skill learning.

Findings

SGIM-D efficiently learns diverse motor outcomes.

The approach benefits from human demonstrations.

It develops precise control policies in large sensorimotor spaces.

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.