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Humanoids learning to crawl based on Natural CPG-Actor-Critic and Motor Primitives
Interaction Lab, University of Skövde, Sweden.
Interaction Lab, University of Skövde, Sweden.
Interaction Lab, University of Skövde, Sweden.
2013 (English)In: Proceedings of IROS 2013 Workshop on Neuroscience and Robotics, Tokyo, Japan / [ed] Emre Ugur, Erhan Oztop, Jun Morimoto, Shin Ishii, 2013, 7-15 p.Conference paper, Published paper (Other academic)
Abstract [en]

In this article, a new CPG-Actor-Critic architecturebased on motor primitives is proposed to perform a crawlinglearning task on a humanoid (the NAO robot). Starting froman  interdisciplinary explanation of the theories, we present twoinvestigations to test the important functions of the layeredCPG architecture: sensory feedback integration and whole-bodyposture control. Based on the analysis of the experimental results,a generic view/architecture for locomotion learning is discussedand introduced in the conclusion.

Place, publisher, year, edition, pages
2013. 7-15 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-106775OAI: oai:DiVA.org:liu-106775DiVA: diva2:718772
Conference
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, November 3-7, 2013, Tokyo Big Sight, Tokyo, Japan
Available from: 2014-05-22 Created: 2014-05-22 Last updated: 2014-05-26Bibliographically approved
In thesis
1. Reinforcement Learning of Locomotion based on Central Pattern Generators
Open this publication in new window or tab >>Reinforcement Learning of Locomotion based on Central Pattern Generators
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Locomotion learning for robotics is an interesting and challenging area in which the movement capabilities of animals have been deeply investigated and acquired knowledge has been transferred into modelling locomotion on robots. What modellers are required to understand is what structure can represent locomotor systems in different animals and how such animals develop various and dexterous locomotion capabilities. Notwithstanding the depth of research in the area, modelling locomotion requires a deep rethinking.

In this thesis, based on the umbrella of embodied cognition, a neural-body-environment interaction is emphasised and regarded as the solution to locomotion learning/development. Central pattern generators (CPGs) are introduced in the first part (Chapter 2) to generally interpret the mechanism of locomotor systems in animals. With a deep investigation on the structure of CPGs and inspiration from human infant development, a layered CPG architecture with baseline motion generation and dynamics adaptation interfaces are proposed. In the second part, reinforcement learning (RL) is elucidated as a good method for dealing with locomotion learning from the perspectives of psychology, neuroscience and robotics (Chapter 4). Several continuous-space RL techniques (e.g. episodic natural actor critic, policy learning by weighting explorations with returns, continuous action space learning automaton are introduced for practical use (Chapter 3). With the knowledge of CPGs and RL, the architecture and concept of CPG-Actor-Critic is constructed. Finally, experimental work based on published papers is highlighted in a path of my PhD research (Chapter 5). This includes the implementation of CPGs and the learning on the NAO robot for crawling and walking. The implementation is also extended to test the generalizability to different morphologies (the ghostdog robot). The contribution of this thesis is discussed from two angles: the investigation of the CPG architecture and the implementation (Chapter 6).

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 71 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1602
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-105884 (URN)978-91-7519-313-7 (ISBN)
Public defence
2014-06-04, G110, hus G, Högskolan i Skövde, Skövde, 12:30 (English)
Opponent
Supervisors
Available from: 2014-05-22 Created: 2014-04-11 Last updated: 2014-05-22Bibliographically approved

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Citation style
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