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A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives
University of Skovde, Sweden.
University of Skovde, Sweden.
Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology. University of Skovde, Sweden.ORCID iD: 0000-0001-6883-2450
2014 (English)In: Frontiers in Neurorobotics, ISSN 1662-5218, Vol. 8Article in journal (Refereed) Published
Abstract [en]

In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a "reshaping" function for adapting the baseline motion to desired motion. Based on this assumption, a three-layer architecture is developed using central pattern generators (CPGs, a bio-inspired locomotor center for the baseline motion) and dynamic motor primitives (DMPs, a model with universal "reshaping" functions). In this article, we use this architecture with the actor-critic algorithms for finding a good "reshaping" function. In order to demonstrate the learning power of the actor-critic based architecture, we tested it on two experiments: (1) learning to crawl on a humanoid and, (2) learning to gallop on a puppy robot. Two types of actor-critic algorithms (policy search and policy gradient) are compared in order to evaluate the advantages and disadvantages of different actor-critic based learning algorithms for different morphologies. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed in the conclusion.

Place, publisher, year, edition, pages
Frontiers , 2014. Vol. 8
Keyword [en]
actor-critic; central pattern generators (CPG); reinforcement learning; locomotion control; NAO robot
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-114450DOI: 10.3389/fnbot.2014.00023ISI: 000348815700001PubMedID: 25324773OAI: oai:DiVA.org:liu-114450DiVA: diva2:789913
Note

Funding Agencies|European RobotDoC project

Available from: 2015-02-20 Created: 2015-02-20 Last updated: 2017-12-04

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Ziemke, Tom

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CiteExportLink to record
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Citation style
  • apa
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