  The co-adaptation of robot morphology and behaviour becomes increasingly
important with the advent of fast 3D-manufacturing methods and efficient deep
reinforcement learning algorithms. A major challenge for the application of
co-adaptation methods to the real world is the simulation-to-reality-gap due to
model and simulation inaccuracies. However, prior work focuses primarily on the
study of evolutionary adaptation of morphologies exploiting analytical models
and (differentiable) simulators with large population sizes, neglecting the
existence of the simulation-to-reality-gap and the cost of manufacturing cycles
in the real world. This paper presents a new approach combining classic
high-frequency deep neural networks with computational expensive Graph Neural
Networks for the data-efficient co-adaptation of agents with varying numbers of
degrees-of-freedom. Evaluations in simulation show that the new method can
co-adapt agents within such a limited number of production cycles by
efficiently combining design optimization with offline reinforcement learning,
that it allows for the direct application to real-world co-adaptation tasks in
future work
