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Crawling Posture Learning in Humanoid Robots using a Natural-Actor-Critic CPG Architecture
Interaction Lab, University of Skövde, Sweden.
Interaction Lab, University of Skövde, Sweden.
Interaction Lab, University of Skövde, Sweden.
2013 (English)In: Advances in Artificial Life, ECAL 2013, Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems / [ed] Pietro Liò, Orazio Miglino, Giuseppe Nicosia, Stefano Nolfi and Mario Pavone, MIT Press, 2013, 1182-1190 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this article, a four-cell CPG network, exploiting sensory feedback, is proposed in order to emulate infant crawling gaits when utilized on the NAO robot. Based on the crawling model, the positive episodic natural-actor-critic architecture is applied to learn a proper posture of crawling on a simulated NAO. By transferring the learned results to the physical NAO, the transferability from simulation to physical world is discussed. Finally, a discussion pertaining to locomotion learning based on dynamic system theory is given in the conclusion.

Place, publisher, year, edition, pages
MIT Press, 2013. 1182-1190 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-106774ISBN: 978-0-262-31719-2 (print)OAI: oai:DiVA.org:liu-106774DiVA: diva2:718770
Conference
The Twelfth European Conference on the Synthesis and Simulation of Living Systems (ECAL 2013), 2-6 September 2013, Taormina, Italy
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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