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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Biologically Inspired Control of a Simulated Octopus Arm via Recurrent Neural Networks |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 978-1-4503-0690-4 |
| Page Range | 21 - 22 |
| Event Title | The Genetic and Evolutionary Computation Conference (GECCO 2011) |
| Event Type | conference |
| Event Location | Dublin, Ireland |
| Event Start Date | July 12 - 2011 |
| Event End Date | July 16 - 2011 |
| Series Name | GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation |
| Place of Publication | New York, NY, USA |
| Publisher | ACM |
| Abstract Text | The aim of this study is to explore a control architecture that can control a soft and "exible octopus-like arm for an object reaching task. Inspired by the division of functionality between the central and peripheral nervous systems of a real octopus, we discuss that the important factor of the control is not to regulate the arm muscles one by one but rather to control them globally with appropriate timing, and we propose an architecture equipped with a recurrent neural network (RNN). By setting the task environment for the reaching behavior, and training the network with an incremental learning strategy, we evaluate whether the network is then able to achieve the reaching behavior or not. As a result, we show that the RNN can successfully achieve the reaching behavior, exploiting the physical dynamics of the arm due to the timing-based control. |
| Free access at | Official URL |
| Official URL | http://dl.acm.org/citation.cfm?id=2001858.2001871 |
| Digital Object Identifier | 10.1145/2001858.2001871 |
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