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

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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.

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Acknowledgments

This work was performed as part of the “Locomorph” project funded by the EU’s Seventh Framework Programme (Future Emerging Technologies, Embodied Intelligence) and as part of the “Assemble and Animate” project funded by the Danish Council for Independent Research.

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Authors and Affiliations

  1. Department of Electrical Engineering, Technical University of Denmark , Kgs. Lyngby, Denmark

    David Johan Christensen

  2. The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark

    Jørgen Christian Larsen & Kasper Stoy

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  1. David Johan Christensen
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  2. Jørgen Christian Larsen
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Correspondence to David Johan Christensen.

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Christensen, D.J., Larsen, J.C. & Stoy, K. Fault-tolerant gait learning and morphology optimization of a polymorphic walking robot. Evolving Systems 5, 21–32 (2014). https://doi.org/10.1007/s12530-013-9088-3

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