Research Output

On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme

  We investigate a hierarchical scheme for the joint optimisation of robot bodies and controllers in a complex morphological space. An evolutionary algorithm optimises body-plans while a separate learning algorithm is applied to each body generated to learn a controller. We investigate the interaction of these processes using a weak and then strong learning method. Results show that the weak learner leads to more body-plan diversity but that both learners cause premature convergence of body-plans to local optima. We conclude with suggestions as the framework might be adapted to address these issues in future.

  • Date:

    07 July 2021

  • Publication Status:

    Published

  • Publisher

    ACM

  • DOI:

    10.1145/3449726.3463156

  • Cross Ref:

    10.1145/3449726.3463156

Citation

Goff, L. K. L., & Hart, E. (2021). On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme. In GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion (1498-1502). https://doi.org/10.1145/3449726.3463156

Authors

Keywords

learning, evolution, morphology, optimisation

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