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Tensegrity robots are based on the idea of tensegrity structures that provides many advantages critical to robotics such as being lightweight and impact tolerant. Unfortunately tensegrity robots are hard to control due to overall complexity. We use multiagent learning to learn controls of a ball-shaped tensegrity with 6 rods and 24 cables. Our simulation results show that multiagent learning can be used to learn an efficient rolling behavior and test its robustness to actuation noise.
@inproceedings{tumer-iscen-tensegrity_aamas13,
author = {A. Iscen and A. Agogino and V. SunSpiral and K. Tumer},
title = {Learning to Control Complex Tensegrity Robots (extended abstract)},
booktitle = {Proceedings of the Twelveth International Joint Conference on Autonomous Agents and Multiagent Systems},
month = {May},
address = {Minneapolis, MN},
abstract={Tensegrity robots are based on the idea of tensegrity structures that provides many advantages critical to robotics such as being lightweight and impact tolerant. Unfortunately tensegrity robots are hard to control due to overall complexity. We use multiagent learning to learn controls of a ball-shaped tensegrity with 6 rods and 24 cables. Our simulation results show that multiagent learning can be used to learn an efficient rolling behavior and test its robustness to actuation noise. },
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Robotics, Evolutionary Algorithms},
year = {2013}
}
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