title: A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives.
abstract: 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.
authors.affiliation: Interaction Lab, School of Informatics, University of Skövde Skövde, Sweden.
authors.forename: Cai
authors.initials: C
authors.lastname: Li
authors.affiliation: Interaction Lab, School of Informatics, University of Skövde Skövde, Sweden.
authors.forename: Robert
authors.initials: R
authors.lastname: Lowe
authors.affiliation: Interaction Lab, School of Informatics, University of Skövde Skövde, Sweden ; Department of Computer and Information Science, Linköping University Linköping, Sweden.
authors.forename: Tom
authors.initials: T
authors.lastname: Ziemke
keywords: NAO robot; actor-critic; central pattern generators (CPG); locomotion control; reinforcement learning