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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.