Title

Single-Unit Pattern Generators For Quadruped Locomotion

Keywords

Gait generation; Generative and developmental systems; HyperNEAT; Neu-roevolution; Oscillation

Abstract

Legged robots can potentially venture beyond the limits of wheeled vehicles. While creating controllers for such robots by hand is possible, evolutionary algorithms are an alternative that can reduce the burden of hand-crafting robotic controllers. Although major evolutionary approaches to legged locomotion can generate oscillations through popular techniques such as continuous time recurrent neural networks (CTRNNs) or sinusoidal input, they typically face a challenge in maintaining long-term stability. The aim of this paper is to address this challenge by introducing an effective alternative based on a new type of neuron called a single-unit pattern generator (SUPG). The SUPG, which is indirectly encoded by a compositional pattern producing network (CPPN) evolved by HyperNEAT, produces a flexible temporal activation pattern that can be reset and repeated at any time through an explicit trigger input, thereby allowing it to dynamically recalibrate over time to maintain stability. The SUPG approach, which is compared to CTRNNs and sinusoidal input, is shown to produce natural-looking gaits that exhibit superior stability over time, thereby providing a new alternative for evolving oscillatory locomotion. Copyright © 2013 ACM.

Publication Date

9-2-2013

Publication Title

GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference

Number of Pages

719-726

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2463372.2463461

Socpus ID

84883096501 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84883096501

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