Self-Adjusting Ring Modules (SARMs) for Flexible Gait Pattern Generation
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 848-851.
Abstract
Using the principle of homeostasis, we derive a learning rule for a specific recurrent neural network structure, the so-called Self-Adjusting Ring Module (SARM). Several of these Ring Modules can be plugged together to drive segmented artificial organisms, for example centipede-like robots. Controlling robots of variable morphologies by SARMs has major advantages over using Central Pattern Generators (CPGs). SARMs are able to immediately reconfigure themselves after reassembly of the robot's morphology. In addition, there is no need to decide on a singular place for the robot's control processor, since SARMs represent inherently distributed control structures.
@inproceedings{ijcai2007p136,
title = {Self-Adjusting Ring Modules (SARMs) for Flexible Gait Pattern Generation},
author = {Hild, Manfred and Pasemann, Frank},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {848--851},
year = {2007},
url = {https://ijcai.org/proceedings/2007/136},
}
TY - CPAPER TI - Self-Adjusting Ring Modules (SARMs) for Flexible Gait Pattern Generation AU - Hild, Manfred AU - Pasemann, Frank T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 848 EP - 851 UR - https://ijcai.org/proceedings/2007/136 ER -Copied
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