Unsupervised behaviour discovery with quality-diversity optimisation
File(s)
Accepted version
Author(s)
Grillotti, Luca
Cully, Antoine
Type
Journal Article
Abstract
Quality-Diversity algorithms refer to a class of evolutionary algorithms designed to find a collection of diverse and high-performing solutions to a given problem. In robotics, such algorithms can be used for generating a collection of controllers covering most of the possible behaviours of a robot. To do so, these algorithms associate a behavioural descriptor to each of these behaviours. Each behavioural descriptor is used for estimating the novelty of one behaviour compared to the others. In most existing algorithms, the behavioural descriptor needs to be hand-coded, thus requiring prior knowledge about the task to solve. In this paper, we introduce: Autonomous Robots Realising their Abilities, an algorithm that uses a dimensionality reduction technique to automatically learn behavioural descriptors based on raw sensory data. The performance of this algorithm is assessed on three robotic tasks in simulation. The experimental results show that it performs similarly to traditional hand-coded approaches without the requirement to provide any hand-coded behavioural descriptor. In the collection of diverse and high-performing solutions, it also manages to find behaviours that are novel with respect to more features than its hand-coded baselines. Finally, we introduce a variant of the algorithm which is robust to the dimensionality of the behavioural descriptor space.
Date Issued
2022-12-01
Date Acceptance
2022-02-28
Citation
IEEE Transactions on Evolutionary Computation, 2022, 26 (6), pp.1539-1552
ISSN
1089-778X
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1539
End Page
1552
Journal / Book Title
IEEE Transactions on Evolutionary Computation
Volume
26
Issue
6
Copyright Statement
Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
Quality-Diversity optimisation
Behavioural Diversity
Unsupervised Machine Learning
Robotics
Optimisation Methods
Publication Status
Published
Date Publish Online
2022-03-16