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Reservoir-based online adaptive forward models with neural control for complex locomotion in a hexapod robot


Manoonpong, Poramate; Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin (2014). Reservoir-based online adaptive forward models with neural control for complex locomotion in a hexapod robot. In: The International Joint Conference on Neural Networks (IJCNN), Beijing, China, 6 July 2014 - 11 July 2014, 3295-3302.

Abstract

Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Simulation results show that this bio-inspired approach allows the walking robot to perform complex locomotor abilities including walking on undulated terrains, crossing a large gap, as well as climbing over a high obstacle and a fleet of stairs.

Abstract

Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Simulation results show that this bio-inspired approach allows the walking robot to perform complex locomotor abilities including walking on undulated terrains, crossing a large gap, as well as climbing over a high obstacle and a fleet of stairs.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:11 July 2014
Deposited On:25 Feb 2015 10:39
Last Modified:15 Sep 2017 18:46
Publisher:IEEE Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN)
Series Name:Neural Networks (IJCNN), 2014 International Joint Conference on
Number of Pages:8
ISBN:978-1-4799-6627-1
Publisher DOI:https://doi.org/10.1109/IJCNN.2014.6889405

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