Journal article
Fault-tolerant gait learning and morphology optimization of a polymorphic walking robot
This paper presents experiments with a morphology-independent, life-long strategy for online learning of locomotion gaits. The experimental platform is a quadruped robot assembled from the LocoKit modular robotic construction kit. 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. We also optimize offline the reachable space of a foot based on a reference design but finds that the reality gap hardens the successfully transference to the physical robot.
To address this limitation, in future work we plan to study co-learning of morphological and control parameters directly on physical robots.
Language: | English |
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Publisher: | Springer Berlin Heidelberg |
Year: | 2013 |
Pages: | 21-32 |
Journal subtitle: | An Interdisciplinary Journal for Advanced Science and Technology |
ISSN: | 18686486 and 18686478 |
Types: | Journal article |
DOI: | 10.1007/s12530-013-9088-3 |
ORCIDs: | Christensen, David Johan |