Fault-tolerant gait learning and morphology optimization of a polymorphic walking robot

David Johan Christensen, Jørgen Christian Larsen, Kasper Stoy

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    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.
    Original languageEnglish
    JournalEvolving Systems
    Issue number1
    Pages (from-to)21–32
    Publication statusPublished - 2013


    • Online learning
    • Locomotion
    • Modular roberts
    • Reconfigurable robots
    • Fault-tolerance
    • Central pattern generators
    • Morphology optimization


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