DMPs based CPG Actor Critic: A Method for Locomotion Learning
2014 (English)Manuscript (preprint) (Other academic)
In this article, a dynamic motor primitives (DMPs) based CPG-Actor-Critic is proposed to enable locomotion learning on a humanoid (the NAO robot) and a puppy robot (the ghostdog robot). In order to model two types of locomotion with one architecture, a novel application of an existing method to designa CPG architecture for learning locomotion. The method is to a) have an architectural base (4-cell CPG) and, b) have a learning component which is based on an existing method for designing DMPs. Learning locomotion here concerns gait emergence in relation to the robot’s body and prior knowledge. The focus of this article will be on two types of locomotion: crawling on ahumanoid and running on a puppy robot. On the two robots with two different morphologies, our method and architecture can make the robots learn by itself. We also compare the performance with respect to two state-of-the-art reinforcement learning algorithms with provided particular instantiations of ourDMPs-based CPG-Actor-Critic architecture. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed and introduced in the conclusion.
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IdentifiersURN: urn:nbn:se:liu:diva-106777OAI: oai:DiVA.org:liu-106777DiVA: diva2:718774