Abstract
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.
in future work we plan to study co-learning of morphological and control parameters directly on physical robots.
Original language | English |
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Journal | Evolving Systems |
Volume | 5 |
Issue number | 1 |
Pages (from-to) | 21–32 |
ISSN | 1868-6478 |
DOIs | |
Publication status | Published - 2013 |
Keywords
- Online learning
- Locomotion
- Modular roberts
- Reconfigurable robots
- Fault-tolerance
- Central pattern generators
- Morphology optimization