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Algorithmic frameworks for the joint optimisation of a robot's design and controller often utilise a learning loop nested within an evolutionary algorithm to refine the controller associated with a newly generated robot design. Intuitively, it is reasonable to assume that the length of the learning period required is directly related to the complexity of the new design. Therefore, we propose a novel self-adaptive criterion that modifies the learning budget for each individual robot based on setting a target for the progress to be achieved during learning. This stopping criterion can lead to wide variance in learning times per robot evaluated. Research in other domains where variable evaluation time is also observed has suggested that asynchronous architectures are preferable in this situation, leading to improved objective performance and efficiency. We conduct a systematic comparison of synchronous and asynchronous …