Conference paper
Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit
This paper presents experiments with a morphologyindependent, life-long strategy for online learning of locomotion gaits, performed on a quadruped robot constructed from the LocoKit modular robot. The learning strategy applies a stochastic optimization algorithm to optimize eight open parameters of a central pattern generator based gait implementation.
We observe that the strategy converges in roughly ten minutes to gaits of similar or higher velocity than a manually designed gait and that the strategy readapts in the event of failed actuators. In future work we plan to study co-learning of morphological and control parameters directly on the physical robot.
Language: | English |
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Publisher: | IEEE |
Year: | 2012 |
Pages: | 63-68 |
Proceedings: | 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems |
ISBN: | 1467317268 , 1467317284 , 9781467317269 and 9781467317283 |
Types: | Conference paper |
DOI: | 10.1109/EAIS.2012.6232806 |
ORCIDs: | Christensen, David Johan |