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Following recent trends, it appears that robot presence within human day-to-day lives is likely to grow and become ubiquitous. As many actors are engaged in this automation effort, it is plausible that the various cultural backgrounds of these actors will result in a broad range of different robots that will nevertheless need to perform similar tasks. Due to the excessively large number of experiences samples needed to successfully train a learning-based control policy, it would be remarkably useful to be able to efficiently transfer the skills acquired by a given agent to other, structurally distinct, robots. Accordingly, the BAM (Base-Abstracted Modeling) methodology proposed in this paper is a fast transfer learning approach that relies on a clear segmentation between the task model, that is a learned policy for solving a specific task and the learned robot control policy. The evaluation on two manipulation tasks using twelve different configurations of mobile manipulators demonstrates the strong potential of this approach as the segmentation results for more robust policies than naive methods and that an efficient transfer can be done in a fraction of the initial training time.

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